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Conference 12031

# Physics of Medical Imaging

In person: 20 - 23 February 2022
View Session
• 1: Breast Imaging
• 2: Detectors
• Sunday/Monday Poster Viewing
• 3: Imaging and Therapy
• 4: Tomosynthesis and Phase Contrast
• Workshop: Photon-Counting CT: Current Status and Future Direction
• 5: X-Ray Imaging
• 6: Photon-Counting Detector and System
• 7: CT New Techniques
• Awards and Plenary Session
• Monday Poster Session
• Posters: Breast Imaging
• Posters: Cone-Beam CT
• Posters: CT and Multi-Energy CT
• Posters: Detectors
• Posters: Image Reconstruction
• Posters: Multi-Modality and Image Processing
• Posters: Phase Contrast
• Posters: Simulation and Phantoms
• Posters: X-ray, Fluoro, and Tomosynthesis
• 8: Spectral CT
• 9: Cone-Beam CT
• 10: Simulation and Phantoms
• 11: Image Reconstruction
• 12: PCD-CT Evaluation and Applications
• 13: CT Image Quality
• 14: Machine Learning in Imaging Physics
• 15: Imaging Physics in Image-Guided Interventions: Joint Session with Conferences 12031 and 12034
Information
This conference is not accepting post-deadline abstract submissions.
Session 1: Breast Imaging
In person: 20 February 2022 • 8:00 AM - 9:40 AM
Session Chairs: Stephen J. Glick, U.S. Food and Drug Administration (United States), Anders Tingberg, Skåne Univ. Hospital (Sweden)
12031-1
Author(s): Mingjie Gao, Jeffrey A. Fessler, Heang-Ping Chan, Univ. of Michigan (United States)
In person: 20 February 2022 • 8:00 AM - 8:20 AM
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To improve digital breast tomosynthesis (DBT) image quality, we investigated the feasibility of DBT reconstruction that combines (1) a model-based iterative reconstruction (MBIR) method that models the detector blur and correlated noise (DBCN) of the DBT system and (2) our previously developed DCNN-based DBT denoiser (DNGAN) as a regularizer. The proposed approach improved the contrast-to-noise ratio, full width at half maximum, and detectability index of the microcalcifications in a set of human subject DBTs compared with DBCN alone or with the unregularized SART algorithm. The soft tissue appearance was visually satisfactory and the background noise level was reduced in the reconstructed images.
12031-2
Author(s): Hanna Tomic, Skåne Univ. Hospital (Sweden); Rebecca Axelsson, Sophia Zackrisson, Lund Univ. (Sweden); Anders Tingberg, Skåne Univ. Hospital (Sweden); Magnus Dustler, Lund Univ. (Sweden); Predrag Bakic, Lund Univ. (Sweden), Univ. of Pennsylvania (United States)
In person: 20 February 2022 • 8:20 AM - 8:40 AM
12031-3
Author(s): Andrey V. Makeev, U.S. Food and Drug Administration (United States); Arthur Emig, Paul Jahnke, Charité Universitätsmedizin (Germany); Stephen J. Glick, U.S. Food and Drug Administration (United States)
In person: 20 February 2022 • 8:40 AM - 9:00 AM
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Digital breast tomosynthesis (DBT) is a 3D breast imaging modality using limited angle image reconstruction which helps to visualize overlapping structures, especially in dense breasts. We are currently developing a methodology for objective task-based assessment of image quality for DBT (and FFDM) systems which includes an anthropomorphic phantom prototype, detection tasks (microcalcifications, low-contrast masses [future work]), and automated performance evaluation using deep-learning model observer (DLMO). Possible use of this tool could be quality control, acceptance and constancy testing, assessing safety and effectiveness of new technology for regulatory submissions, and performance comparison to prior products. In this work we report experimental results with our breast phantom and microcalcification signals conducted on the clinical FFDM/DBT system.
12031-4
Author(s): Xiaoyu Duan, Adrian Howansky, Hailiang Huang, Wei Zhao, Stony Brook Medicine (United States)
In person: 20 February 2022 • 9:00 AM - 9:20 AM
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We proposed a direct-indirect dual layer detector combination for CEDM and CEDBT to eliminate patient motion artifacts in dual energy images. Both physical experiments and Monte Carlo simulations were conducted to compare dual shot technique with the dual layer technique. The proposed direct-indirect dual layer detector combination uses a 200 µm direct a-Se as the front detector for LE images and a 400~1000 µm indirect CsI as the back detector for HE images. Results showed similar breast tissue cancellation between dual layer and dual shot techniques. Dual layer technique with direct-indirect detector has higher SDNR/MGD compared with dual shot technique.
12031-5
Author(s): Colin Schaeffer, Univ. of Florida (United States), U.S. Food and Drug Administration (United States); Bahaa Ghammraoui, U.S. Food and Drug Administration (United States); Katsuyuki Taguchi, Johns Hopkins University School of Medicine (United States); Stephen Glick, U.S. Food and Drug Administration (United States)
In person: 20 February 2022 • 9:20 AM - 9:40 AM
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There has recently been renewed interest in quantitative contrast-enhanced spectral mammography. Most photon-counting detectors (PCDs) and prototype systems use CdTe or CZT sensor material which have non-optimal characteristic X-ray emission with energies in the range used for breast imaging. Recently, a new PCD has been developed using a GaAs sensor. Since GaAs has lower energy characteristic x-rays, it is expected that this new PCD detector might be beneficial for spectral x-ray breast imaging. In this work, we have theoretically compared the two detector materials in terms of iodine quantification using the Cramer-Rao lower bound as a figure of merit.
Session 2: Detectors
In person: 20 February 2022 • 10:10 AM - 12:10 PM
Session Chairs: Karim Salaudin Karim, Univ. of Waterloo (Canada), Shiva Abbaszadeh, Univ. of California, Santa Cruz (United States)
12031-6
Author(s): Kaitlin Hellier, Univ. of California, Santa Cruz (United States); Emmie Benard, Arizona State Univ. (United States); Shiva Abbaszadeh, Univ. of California, Santa Cruz (United States)
In person: 20 February 2022 • 10:10 AM - 10:30 AM
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Amorphous selenium indirect detectors are common in medical imaging, with scintillator emission in blue wavelengths. However, the emission tail is often not converted by a-Se absorption. We propose a-Se lateral devices doped as a function of depth with Te (0-30%) to generate sensitivity from UV to red wavelengths for indirect detection. In this work, we present the first steps in the development of these devices by studying the transport properties of a-Se/Te single layer devices of different dopant levels. We report hole and electron mobilities in vertical and lateral devices with optical slits found by time of flight.
12031-7
Author(s): Stefan van der Sar, Technische Univ. Delft (Netherlands); Stefan Brunner, Broadcom Inc. (Germany); Dennis Schaart, Technische Univ. Delft (Netherlands), Holland Proton Therapy Ctr (Netherlands)
In person: 20 February 2022 • 10:30 AM - 10:50 AM
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We investigate X-ray photon-counting scintillation detectors with silicon photomultiplier (SiPM) readout. These circumvent some drawbacks of direct-conversion detectors. We measured observed count rate (OCR) versus X-ray tube current for single-pixel detectors consisting of LYSO:Ce and YAP:Ce scintillators coupled to ultrafast SiPMs. For a 30 keV threshold, the maximum OCRs equal 4.5 Mcps/pixel (LYSO:Ce) and 5.5 Mcps/pixel (YAP:Ce) for paralyzable-like counting and 10 Mcps/pixel (LYSO:Ce) and 12.5 Mcps/pixel (YAP:Ce) for nonparalyzable-like counting. We estimate that the twice as fast LaBr3:Ce scintillator yields OCRs approaching those of CdTe/CZT-based photon-counting CT detectors. We also show energy response data and discuss dose-efficient pixel miniaturization.
12031-8
Author(s): Sarah Deumel, Siemens Healthineers (Germany); Albert J. J. M. van Breemen, Bart Peeters, Joris Maas, Hylke B. Akkerman, Eric A. Meulenkamp, Gerwin H. Gelinck, Holst Ctr. (Netherlands); Judith E. Huerdler, Oliver Schmidt, Siemens Healthineers (Germany); Wolfgang Heiss, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany); Sandro F. Tedde, Siemens Healthineers (Germany)
In person: 20 February 2022 • 10:50 AM - 11:10 AM
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The next generation medical imaging will benefit significantly from artificial intelligence – therefore, not only advances in computing power are required, but also further materials and technology improvements will lead to better image quality. The strong X-ray absorption, high charge carrier mobility and lifetime recommend perovskites like methylammonium lead triiodide (MAPbI3) as novel direct X-ray converting materials. A major obstacle to the commercialization is the limited stability and lifetime, as reported until now. Here, we show for the first time that degradation is limited in our X-ray detector after 1.5 years of storage in ambient conditions.
12031-9
Author(s): Xinlin Wu, Allison Shields, S. V. Setlur Nagesh, Daniel Bednarek, Buffalo Clinical and Translational Research Ctr. (United States); Stephen Rudin, Buffalo Clinical and Translational Research Center (United States)
In person: 20 February 2022 • 11:10 AM - 11:30 AM
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High Speed Angiography requires imaging detectors with both high temporal and spatial resolution. The Aries is a new Anti-Coincidence Circuit CdTe photon counting detector which offers a large FOV 1000 fps acquisition. Instrumentation noise and gamma spectroscopy calibration was evaluated for the Aries. Linearity was evaluated for the whole detector and for each of the individual 12 modules that compose the detector field. Finally, Normalized Noise Power Spectrum, Modulation Transfer Function and Detective Quantum Efficiency were then compared between the Aries 512x768 100-micron pixel detector and the Actaeon 256x256 100-micron pixel 1000 fps PCD detector.
12031-10
Author(s): Liuxing Shen, Youcef El-Mohri, Qihua Zhao, Larry E. Antonuk, Univ. of Michigan (United States)
In person: 20 February 2022 • 11:30 AM - 11:50 AM
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The degradation in DQE performance of active matrix flat-panel imagers at low doses in applications such as digital breast tomosynthesis can be overcome through use of polycrystalline mercuric iodide fabricated using particle-in-binder techniques (PIB-HgI2) which offers 3 to 10 times more imaging signal than CsI:Tl and a-Se. While PIB-HgI2 exhibits prohibitively high levels of image lag, the incorporation of a Frisch grid into the converter material can suppress imaging signal induced by hole transport through judicious choice of grid design. In the present theoretical study, such suppression is shown to be accompanied by a corresponding, significant reduction in image lag.
12031-11
In person: 20 February 2022 • 11:50 AM - 12:10 PM
Sunday/Monday Poster Viewing
In person: 20 February 2022 • 12:00 PM - 7:00 PM
Posters will be on display Sunday and Monday with extended viewing until 7:00 pm on Sunday. The poster session with authors in attendance will be Monday evening from 5:30 to 7:00 pm. Award winners will be identified with ribbons during the reception. Award announcement times are listed in the conference schedule.
Session 3: Imaging and Therapy
In person: 20 February 2022 • 1:20 PM - 3:00 PM
Session Chairs: Wei Zhao, Stony Brook Univ. (United States), Maria Drangova, Robarts Research Institute (Canada)
12031-500
Author(s): Katherine W. Ferrara, Univ. of California, Davis (United States), Stanford Univ. (United States)
In person: 20 February 2022 • 1:20 PM - 2:20 PM
12031-12
Author(s): Joseph Bae, Renee Cattell, Ewa Zabrocka, Stony Brook Univ. (United States); John Roberson, Southeast Radiation Oncology Group (United States); David Payne, Kartik Mani, Prateek Prasanna, Stony Brook Univ. (United States)
In person: 20 February 2022 • 2:20 PM - 2:40 PM
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Stereotactic radiosurgery is commonly employed to treat brain metastases. However, >50% of patients treated with this method will develop distant brain metastases (DBMs). Currently, there is no risk metric to determine which patients are at greater risk for DBM development prior to treatment. Leveraging a unique dataset of pre-treatment multiparametric MRIs and radiotherapy planning data, we demonstrate that the identification of tumor subcompartments based upon different regions of therapeutic radiation yields radiomic features that are predictive of DBM development. Machine learning classifiers trained using these features from pre-treatment MRIs outperform models using clinical variables such as age and performance status.
12031-13
Author(s): Kevin Treb, Xu Ji, Sarvesh Periyasamy, Mang Feng, Ran Zhang, Dan Bushe, Paul Laeseke, Ke Li, Univ. of Wisconsin-Madison (United States)
In person: 20 February 2022 • 2:40 PM - 3:00 PM
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C-arm x-ray systems with flat panel detectors (FPD) lack spectral and ultra-high-resolution capabilities desired for image guided interventions, which can be provided using photon counting detectors (PCDs). We propose a new “dagger” PCD design for IGIs that preserves the functionality of the FPD and reduces the cost compared to a large-area PCD. The design consists of a strip-shaped module for narrow-beam CT with full axial coverage, and a rectangle-shaped module for region-of-interest 2D and 3D imaging. Prototypes of each module were mounted onto a C-arm gantry: Experiments show the designs potential for spectral and ultra-high-resolution 2D and 3D imaging.
Session 4: Tomosynthesis and Phase Contrast
In person: 20 February 2022 • 3:30 PM - 5:30 PM
Session Chairs: Ioannis Sechopoulos, Radboud Univ. Medical Ctr. (Netherlands), Yongshuai Ge, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (China)
12031-14
Author(s): Kian Shaker, Ilian Häggmark, KTH Royal Institute of Technology (Sweden); Sven Nyrén, Bariq Al-Amiry, Karolinska Institute (Sweden); Ehsan Abadi, William P. Segars, Ehsan Samei, Duke Univ. (United States); Hans M. Hertz, KTH Royal Institute of Technology (Sweden)
In person: 20 February 2022 • 3:30 PM - 3:50 PM
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We present the first investigation of propagation-based phase-contrast chest X-ray (CXR) imaging. Conventional and phase-contrast CXR images of virtual patients were generated by applying wave-propagation simulations to XCAT virtual chest phantoms. We then performed a reader study with clinical radiologists to investigate the potential clinical impact of phase-contrast CXR. No significant improvement in lesion (6-20 mm) detection rate was found, however, phase-contrast CXR proved to visualize airways that are invisible in conventional CXR. This could have clinical significance for diagnosing diseases presenting a thickening of airway walls, such as in cases of chronic obstructive pulmonary disease (COPD).
12031-15
Author(s): Daniel H. Bushe, Xu Ji, Ran Zhang, Mang Feng, Kevin Treb, Guang-Hong Chen, Ke Li, Univ. of Wisconsin-Madison (United States)
In person: 20 February 2022 • 3:50 PM - 4:10 PM
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The purpose of this work is develop a novel multi-contrast chest x-ray radiography (MC-CXR) imaging system to enable the simultaneous generation of three mutually complementary x-ray contrast mechanisms to enhance the diagnostic performance of CXR for respiratory diseases. The developed grating-based MC-CXR system employs a scanning beam image acquisition scheme in which the patient table is translated at a speed of up to 9 cm/s. The system is capable of accomplishing MC-CXR imaging of an anthropomorphic chest phantom in under 4 seconds, with an air kerma and effective dose that are well below that of a conventional CXR exam.
12031-16
Author(s): Marta C. Pinto, Koen Michielsen, Alejandro Rodríguez-Ruiz, Radboud Univ. Medical Ctr. (Netherlands); Ramyar Bianiazan, Steffen Kappler, Siemens Healthcare GmbH (Germany); Ioannis Sechopoulos, Radboud Univ. Medical Ctr. (Netherlands)
In person: 20 February 2022 • 4:10 PM - 4:30 PM
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We present a deep learning model capable of predicting scatter in digital breast tomosynthesis, based on Monte-Carlo simulations of realistically shaped phantoms. The model used homogeneous phantoms for training, validation and testing, with final mean relative and absolute errors of 0.54% and 2.05% between prediction and ground truth. Its generalizability was further evaluated on phantoms with internal glandular structure, where the measured errors were -0.51% and 4.95%, respectively. These results indicate that the model trained on homogenous phantoms captures the average scatter representation reasonably well even when internal structure is present, but with decreasing accuracy.
12031-17
Author(s): Michela Esposito, Lorenzo Massimi, Ian Buchanan, Univ. College London (United Kingdom); Joseph D. Ferrara, Rigaku Americas Corp. (United States); Marco Endrizzi, Alessandro Olivo, Univ. College London (United Kingdom)
In person: 20 February 2022 • 4:30 PM - 4:50 PM
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A novel multi-modal phase-based x-ray microscope for tissue imaging with applications in biomedical research and clinical practice (histopathology) will be presented. The microscope is capable of imaging unstained mm-thick tissue samples on mm-size field of view using intensity-modulation masks which allow the quantitative retrieval of three contrast channels, namely transmission, refraction and scattering. The resolution of the microscope depends only on the mask aperture size, leading to the possibility of a multi-resolution approach to match the resolution requirements of specific samples. The optimization of the system parameters will be presented along with exemplar images retrieved for the three contrast channels.
12031-18
Author(s): Hyeongseok Kim, KAIST (Korea, Republic of); Hoyeon Lee, Massachusetts General Hospital (United States); Seoyoung Lee, KAIST (Korea, Republic of); Young-Wook Choi, Young Jin Choi, Kee Hyun Kim, Korea Electrotechnology Research Institute (Korea, Republic of); Wontaek Seo, Choul Woo Shin, DRTECH Corp. (Korea, Republic of); Seungryong Cho, KAIST (Korea, Republic of)
In person: 20 February 2022 • 4:50 PM - 5:10 PM
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While breast density is known as one of the critical risk factors of breast cancer, Digital breast tomosynthesis (DBT)-based diagnostic performance is known to have a strong dependence on breast density. As a potential solution to increase the diagnostic performance of DBT, we are investigating dual-energy DBT imaging techniques. We estimated partial path lengths of an x-ray through water, lipid, and protein from the measured dual-energy projection data and the object thickness information. We reconstructed material-selective DBT images for the material-decomposed projection. The feasibility of the proposed dual-energy DBT scheme has been demonstrated by using physical phantoms.
12031-19
Author(s): Priyash Singh, Chloe J. Choi, Trevor L. Vent, Bruno Barufaldi, Raymond J. Acciavatti, Emily F. Conant, Andrew D. A. Maidment, Univ. of Pennsylvania (United States)
In person: 20 February 2022 • 5:10 PM - 5:30 PM
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The mathematical underpinnings of a novel reconstruction algorithm are presented that can facilitate 4D tomosynthesis for the purpose of guiding needle breast biopsies in real-time. Conventional tomosynthesis reconstruction algorithms produce motion artifacts when applied to a continuous tomosynthesis acquisition of a moving biopsy needle. The novel algorithm proposed in this work successfully overcomes this by using differences in slow-scan data to identify variational regions in the reconstructed volume, and adaptively reconstruct those regions to eliminate motion. The algorithm has been tested using simulated images, where reconstructed images of a moving needle had significantly better clarity than the conventional algorithm.
Session WK1: Workshop: Photon-Counting CT: Current Status and Future Direction
In person: 20 February 2022 • 5:45 PM - 7:45 PM
Session Chair: Wei Zhao, Stony Brook Univ. (United States)
Session 5: X-Ray Imaging
In person: 21 February 2022 • 8:00 AM - 9:40 AM
Session Chairs: Hilde Bosmans, UZ Leuven (Belgium), John M. Sabol, GE Healthcare (United States)
12031-20
Author(s): Ilwoong Choi, Ibragim Atadjanov, Wontaek Seo, Mijung Jo, Junekyu Park, Jeongmin Ahn, Taehee Kim, Hyunjong Kim, Choul Woo Shin, DRTECH Corp. (Korea, Republic of)
In person: 21 February 2022 • 8:00 AM - 8:20 AM
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This work proposes a robust method of identifying the optimal cancellation parameters for dual-energy imaging. A classification predictive modeling based on XGBoost was employed to identify optimal cancellation parameters for soft-tissue and bone selected images. The optimal parameters can be obtained by selecting the parameter of the highest probability predicted by the model. The performance of the predicted cancellation parameters and the optimal values determined by well-trained observers was evaluated to verify the robustness of the proposed method. This work was designed in a simple and practical way to improve dual-energy imaging by providing robust identifying the optimal cancellation parameters. In order to achieve robustness, we focused on and utilized histogram information rather than spatial information of images. According to the robust results of our proposed method, the value of this work can contribute to advancements in chest x-ray imaging technologies.
12031-21
Author(s): Sora Park, Electronics and Telecommunications Research Institute (Korea, Republic of); Yoon-Ho Song, Electronics and Telecommunications Research Institute (Korea, Republic of), ETRI ICT School, Univ. of Science and Technology (Korea, Republic of); Jin-Woo Jeong, Jae-Woo Kim, Jun-Tae Kang, Ki Nam Yun, Sunghoon Choi, Electronics and Telecommunications Research Institute (Korea, Republic of); Eunsol Go, Jeong-Woong Lee, Yujung Ahn, ETRI ICT School, Univ. of Science and Technology (Korea, Republic of), Electronics and Telecommunications Research Institute (Korea, Republic of); Ji-Hwan Yeon, Sunghee Kim, Electronics and Telecommunications Research Institute (Korea, Republic of)
In person: 21 February 2022 • 8:20 AM - 8:40 AM
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We have developed an ultra-low-dose fluoroscopy system with real-time frame rate modulation using a fully digital x-ray tube based on CNT field electron emitters. The x-ray tube was manufactured in a fully vacuum-sealed type, and the maximum anode voltage and emission current were 120 kV and 30 mA, respectively. By applying manufactured x-ray tube, we produced the digital x-ray source monoblcok for an ultra-low-dose fluoroscopy system which allows to modulate the x-ray pulse frame and dose rate in the range of 1-30 Hz in real-time. We analyzed the x-ray dose, and achieved remarkable 54 % reduction of x-ray dose and improvement of temporal and spatial resolution of images due to the fully digital x-ray pulse signal with rise/fall, resulting in reduction of unnecessary radiation damage and improvement of the qualities of still and moving x-ray images.
12031-22
Author(s): Ethan P. Nikolau, Joseph F. Whitehead, Martin G. Wagner, Paul F. Laeseke, Michael A. Speidel, Univ. of Wisconsin-Madison (United States)
In person: 21 February 2022 • 8:40 AM - 9:00 AM
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A prototype dual-energy C-arm system with kV-switching capability is under development for 2D and 3D interventional imaging. This study evaluated methods for noise reduction in 2D images from this platform. Methods investigated were standard correlated noise reduction (ACNR), which exploits correlations in iodine/bone-only and tissue-only images, and a hybrid algorithm (ACNR-ML) that employs machine learning to achieve further reduction in low-frequency noise compared to ACNR. Iodine signal-difference-to-noise (SDNR) and vessel FWHM were measured from dual-energy images of an anthropomorphic phantom. Compared to non-denoised images, the ACNR-ML algorithm yielded a several-fold SDNR improvement and noise texture comparable to native x-ray projections.
12031-23
Author(s): Nicholas Marshall, Univ. Hospitals Leuven (Belgium); Michiel Dehairs, Institut Jules Bordet (Belgium); Hannelore Verhoeven, Hilde Bosmans, Univ. Hospitals Leuven (Belgium)
In person: 21 February 2022 • 9:00 AM - 9:20 AM
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Antiscatter grid performance is usually evaluated via explicitly measured grid parameters, however this may not fully characterize the change in dose efficiency when the grid is incorporated in an imaging system. This study evaluates the influence of a new high aspect ratio grid for coronary angiography applications on system dose efficiency using a spatial frequency domain-based figure of merit. The new grid is found to increase dose efficiency by between 10% and 48% compared to the grid currently used on the angiography system, for phantom thicknesses above ~24 cm.
12031-24
Author(s): Linxi Shi, N. Robert Bennett, Adam S. Wang, Stanford Univ. School of Medicine (United States)
In person: 21 February 2022 • 9:20 AM - 9:40 AM
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A new algorithm for single-shot quantitative x-ray imaging(SSQI) is proposed which achieves accurate quantification and high computational efficiency. The SSQI is enabled by using a primary modulator (PM) and dual-layer (DL) detector, where the PM performs scatter correction for the DL images, while the DL images remove beam hardening from the PM. Using the low-frequency property of scatter and a pre-calibrated material decomposition (MD), the scatter images and material-specific images can be jointly estimated. We tested this algorithm on simulation and further demonstrated its efficacy using chest phantom experiments. The reported results further strengthen the potential of SSQI for widespread adoption, leading to quantitative imaging not only for x-ray imaging but also for real-time image guidance or cone-beam CT.
Session 6: Photon-Counting Detector and System
In person: 21 February 2022 • 10:10 AM - 12:10 PM
Session Chairs: Mats Danielsson, KTH Royal Institute of Technology (Sweden), Adam S. Wang, Stanford Univ. School of Medicine (United States)
12031-25
Author(s): Christer Ullberg, Mattias Urech, Leif Adelöw, Niclas Weber, Direct Conversion AB (Sweden)
In person: 21 February 2022 • 10:10 AM - 10:30 AM
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We demonstrate the performance of a new fast photon counting detector with six energy thresholds and 150µm pixel size that can be read out up to 10000fps. It is a four side buttable device that is bump bonded to a CdTe converter. The detector is effective all the way out to the edge, which opens up the possibility to build larger detectors without gaps. The detector incorporates a charge sharing correction feature and the effect of this function is demonstrated using the DQE measurement and spectrum reconstruction from radioactive sources.
12031-26
Author(s): Katsuyuki Taguchi, Johns Hopkins Medicine (United States); Christoph Polster, Siemens Healthineers (Germany); William P. Segars, Duke Univ. (United States); Nafi Aygun, Johns Hopkins Medicine (United States); Karl Stierstorfer, Siemens Healthineers (Germany)
In person: 21 February 2022 • 10:30 AM - 10:50 AM
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Many CT applications that use photon counting detectors (PCDs) rely on accurate spectral information; thus, the performance could be limited when spectra are distorted due to pulse pileup (PP) and charge sharing (CS). Due to complex mechanisms of PP and CS and its combination, to our knowledge, no model-based algorithm has been developed to compensate for the effect of PP and CS. Thus, the aim of this study was to develop such an algorithm. The computer simulations using slabs, chest/heart, and head-and-neck areas showed that the proposed 3-step exhaustive search algorithm with the PP–CS model can address the spectral distortion in PCD.
12031-27
Author(s): Xiangyang Tang, Emory Univ. (United States); Yan Ren, Emory Univ. School of Medicine (United States); Huiqiao Xie, Emory Univ. (United States)
In person: 21 February 2022 • 10:50 AM - 11:10 AM
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We propose the data acquisition scheme with interleaved/gapped spectral channelization (energy binning) for spectral imaging in photon-counting CT. The quantitative study is carried out via computer simulation and reveals (i) under ideal detector spectral response, the scheme (ch1, ch4) outperforms the benchmark scheme ((ch1 + ch2), (ch3 + ch4)) and other gapped and/or interleaved spectral channelization schemes in material specific imaging, while the interleaved scheme (ch1, ch4) + (ch2, ch3) performs the best in virtual monochromatic imaging; (ii) Under realistic detector spectral response, the differences in imaging performance between all spectral channelization schemes diminishes, along with degradation in each scheme’s individual performance. The proposed data acquisition scheme with interleaved/gapped spectral channelization is innovative and provides guidelines on photon-counting CT’s design and implementation.
12031-28
Author(s): Yirong Yang, Sen Wang, Stanford Univ. (United States); Debashish Pal, GE Healthcare (United States); Norbert Pelc, Adam Wang, Stanford Univ. (United States)
In person: 21 February 2022 • 11:10 AM - 11:30 AM
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Photon counting detectors provide spectral information through energy bins, but data transmission across the slip ring is challenging due to the increased amount of data. We propose a projection-domain energy bin weighting method that produces two energy-weighted measurements that provide comparable spectral information as the original binned counts for material decomposition and virtual monoenergetic imaging tasks. The two empirically-derived energy-weighted measurements provide comparable material decomposition results with low bias and less than 20% variance penalty for a large range of patient sizes, with a data reduction of 75% for a silicon detector with 8 energy bins.
12031-29
Author(s): Ahmet Camlica, Univ. of Waterloo (Canada); Denny Lee, Direct-Xray Digital Imaging (United States); Hyunsuk Jang, Vieworks Co., Ltd. (Korea, Republic of); M. Zahangir Kabir, Concordia Univ. (Canada); Karim Karim, Univ. of Waterloo (Canada)
In person: 21 February 2022 • 11:30 AM - 11:50 AM
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In this study, we fabricated a pixelated unipolar charge sensing detector based on amorphous selenium with a 20-μm pixel pitch using standard lithography process. A pulse-height spectroscopy (PHS) setup with a very low noise front-end electronics was designed, and experiments were performed to investigate the achievable energy resolution with the unipolar detector, as well as with a conventional detector for comparison purposes. PHS measurement results are presented that demonstrate, for the first time, a measured energy resolution of 8.3 keV at 59.5 keV is for the unipolar charge sensing device in contrast to 14.5 keV at 59.5 keV for conventional a-Se devices, indicating its promise for the contrast-enhanced photon counting imaging with an unsurpassed spatial resolution.
12031-30
Author(s): Sen Wang, Yirong Yang, Stanford Univ. (United States); Debashish Pal, GE Healthcare (United States); Norbert Pelc, Adam Wang, Stanford Univ. (United States)
In person: 21 February 2022 • 11:50 AM - 12:10 PM
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Photon counting x-ray detectors enable spectral imaging, which can be utilized for material decomposition and quantitative imaging tasks. This study investigates potential benefits of tube voltage optimization, including fast kV switching, on decomposition noise. In simulation studies, single kV scans including 80/100/120/140 kV were tested as well as an 80/140 fast kV switching pair. Fluences of spectra were normalized based on CTDI to keep dose neutral. An open-source spectral response model of a realistic Si detector was used in the CRLB calculation, which estimates the lower bound of the noise. A simulated CT scan of a head phantom was performed for noise analysis in the image domain. Simulation results showed that a single kV can be optimized for specific imaging tasks depending on the object size, while fast kV switching can substantially reduce the noise in material decomposition compared to single kV scan. Noise can be further reduced with a fixed K-edge (Gd) filter.
Session 7: CT New Techniques
In person: 21 February 2022 • 1:20 PM - 3:40 PM
Session Chairs: Marc Kachelriess, Deutsches Krebsforschungszentrum (Germany), Ke Li, Univ. of Wisconsin-Madison (United States)
12031-31
Author(s): Nadav Shapira, Kai Mei, Michael Geagan, Leonid Roshkovan, Harold I. Litt, Univ. of Pennsylvania (United States); Grace J. Gang, Web Stayman, Johns Hopkins Univ. (United States); Peter B. Noël, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 1:20 PM - 1:40 PM
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We introduce PixelPrint, a 3D-printing solution to create patient-based lung CT phantoms with realistic contrast and textures. Phantoms based on CT images of patients with COVID-19 pneumonia were manufactured and scanned using the original CT scanner and protocol. Qualitatively, images of the patient-based phantoms closely resemble the original clinical images. Attenuation differences below 15 HU, size differences below the intrinsic spatial resolution, and radiomic feature analysis, all showed high correspondence between patient and 3D-printed phantoms. This demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate geometry, texture, and contrast, enabling protocol optimization and advanced CT research and development applications.
12031-32
Author(s): Scott S. Hsieh, Shuai Leng, Lifeng Yu, Cynthia H. McCollough, Mayo Clinic (United States); Adam S. Wang, Stanford Univ. (United States)
In person: 21 February 2022 • 1:40 PM - 2:00 PM
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Fluence field modulation (FFM) using dynamic pre-patient attenuators could reduce radiation dose while preserving image quality. Past dynamic attenuators require mechanical action that is challenging to implement. To circumvent these difficulties, we propose a motion-free mechanism for FFM that uses electromagnetic deflection of the focal spot, also called flying focal spot (FFS), together with interference patterns generated from fixed metal gratings. This design allows a limited number of base fluence fields, and intermediate fluence fields can be virtually generated during reconstruction.
12031-33
Author(s): Matthew Tivnan, Wenying Wang, Grace Gang, Johns Hopkins Univ. (United States); Peter Noël, Univ. of Pennsylvania (United States); J. Webster Stayman, Johns Hopkins Univ. (United States)
In person: 21 February 2022 • 2:00 PM - 2:20 PM
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The most common loss function used to train neural networks for CT data processing is mean-squared error which penalizes both variance and bias but does not offer any control over the trade-off between the two. In this work, we propose a method for controlling the output image properties of neural networks with a new class of loss functions called task-weighted variance and bias (TVB). Our proposed method includes separate weighting parameters to control the relative importance of variance or bias reduction. To evaluate our method, we present a simulation study involving the generation of digitial anthropormorphic phantoms, simulation of non-ideal CT data, and image formation with various algorithms. We show that TVB offers a greater degree of control over bias-variance trade-offs whereas MSE has only one configuration. We also show that TVB can be used to control specific image properties including variance, bias, spatial resolution, and noise correlation of neural network outputs.
12031-34
Author(s): Hao Gong, Scott S. Hsieh, David Holmes, David Cook, Akitoshi Inoue, David Bartlett, Francis Baffour, Hiroaki Takahashi, Shuai Leng, Lifeng Yu, Cynthia McCollough, Joel Fletcher, Mayo Clinic (United States)
In person: 21 February 2022 • 2:20 PM - 2:40 PM
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Eye-tracking techniques can be used to understand the visual search process in diagnostic radiology. Nonetheless, most prior eye-tracking studies in CT only involved single cross-sectional images or video playback of the reconstructed volume and meanwhile applied strong constraints to reader-image interactivity, yielding a disconnection between the corresponding experimental setup and clinical reality. To suppress this limitation, we developed an eye-tracking system that integrates eye-tracking hardware with in-house-built image viewing software. This system enabled recording of radiologists’ real-time eye-movement and interactivity with the displayed images in clinically relevant tasks. In this work, the implementation and initial experiences with this system are demonstrated.
12031-35
Author(s): Abdullah-Al-Zubaer Imran, Stanford Univ. (United States); Debashish Pal, GE Healthcare (United States); Sen Wang, Stanford Univ. (United States); Sandeep Dutta, GE Healthcare (United States); Evan Zucker, Adam Wang, Stanford Univ. (United States)
In person: 21 February 2022 • 2:40 PM - 3:00 PM
12031-36
Author(s): Yueting Luo, Derrek Spronk, Alex Billingsley, Christina R. Inscoe, Yueh Z. Lee, Otto Zhou, Jianping Lu, The Univ. of North Carolina at Chapel Hill (United States)
In person: 21 February 2022 • 3:00 PM - 3:20 PM
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The CNT-enabled x-ray sources have unique advantages compare to traditional x-ray sources. CNT x-ray source array enabled stationary breast, chest, and intraoral tomosynthesis imaging systems have been evaluated and demonstrated to be superior to rotation gantry tomosynthesis systems. In this work, we reported the progress on a novel stationary head CT system (sHCT) with three CNT x-ray source arrays. The proposed sHCT system was implemented successfully with three parallel imaging planes separated along the axial direction with the same spacing between neighboring planes. The three-plane sHCT system can be applied in volumetric head imaging with continuous motion of the object and producing good quality CT images. The result gave us confidence in future clinical applications.
12031-37
Author(s): Scott S. Hsieh, Lifeng Yu, Shuai Leng, Cynthia H. McCollough, Mayo Clinic (United States)
In person: 21 February 2022 • 3:20 PM - 3:40 PM
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We estimate the minimum SNR necessary for object detection in the projection domain. We assume there is a set of objects O and we study an ideal observer that sequentially compares each member of O to the null hypothesis. This reduces to one-dimensional signal detection between two Gaussians. We find that for a search task of a circular 6 mm lesion in a region of interest 60 mm by 60 mm by 10 slices, and for a required sensitivity of 80% and specificity of 80%, the minimum required projection SNR is 5.1, a finding reminiscent of the Rose criterion.
Awards and Plenary Session
In person: 21 February 2022 • 4:00 PM - 5:15 PM
Session Chairs: Metin N. Gurcan, Wake Forest Baptist Medical Ctr. (United States), Robert M. Nishikawa, Univ. of Pittsburgh (United States)
4:00 pm: Symposium Chair Welcome and best Student Paper Award Announcement
The first place winner and runner up of the Robert F. Wagner All-Conference Student Paper Award will be announced.
4:15 pm: SPIE 2022 Presidents Welcome and new SPIE Fellows Acknowledgements
4:20 pm: SPIE Harrison H. Barrett Award in Medical Imaging
This award will be presented in recognition of outstanding accomplishments in medical imaging.
12032-300
Author(s): Jennifer N. Avari Silva, Washington Univ. in St. Louis (United States)
In person: 21 February 2022 • 4:30 PM - 5:15 PM
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With the increased availability of extended reality (XR) devices in the marketplace, there has been a rapid development of medical XR applications spanning from education, training, rehabilitation, pre-procedural planning, and intra-procedural use. We will explore various use case to understand the importance of technology-use case matches and focus on intra-procedural use cases which generally have the highest risk to patient and medical provider but may have the most sizable impact on benefit to patient and procedure.
Monday Poster Session
In person: 21 February 2022 • 5:30 PM - 7:00 PM
All symposium attendees are invited to attend the evening Monday Poster Session to view the high-quality posters and engage the authors in discussion. Attendees are required to wear their conference registration badges to access the Poster Session. Authors may set up their posters starting Sunday 20 February.*

*In order to be fully considered for a Poster Award, it is recommended to have your poster set up by 12:00pm on Sunday 20 February 2022. Posters should remain on display until the end of the Poster Session on Monday.
Posters: Breast Imaging
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-77
Author(s): Zan Klanecek, Univ. of Ljubljana (Slovenia); Tobias Wagner, Yao Kuan Wang, KU Leuven (Belgium); Lesley Cockmartin, UZ Leuven (Belgium); Kristijana Hertl, Mateja Krajc, Institute of Oncology, Ljubljana (Slovenia); Nicholas Marshall, UZ Leuven (Belgium), KU Leuven (Belgium); Andrej Studen, Univ. of Ljubljana (Slovenia), Jožef Stefan Institute (Slovenia); Miloš Vrhovec, Institute of Oncology, Ljubljana (Slovenia); Hilde Bosmans, KU Leuven (Belgium), UZ Leuven (Belgium); Robert Jeraj, Univ. of Ljubljana (Slovenia), Univ. of Wisconsin-Madison (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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This work studies the impact of parameter settings on the values of extracted radiomic features from digital mammograms. Since the behavior of radiomic features can be dependent on the parameters, it is important to choose parameter values carefully. Furthermore, candidate radiomic features for any new breast imaging application should have at least 2 characteristics: similar breasts should have similar feature values (biological reproducibility) and breasts with different conditions for the application under study should yield different feature values (biological sensitivity). A comprehensive analysis revealed that with certain parameter settings it is possible to increase biological reproducibility, while retaining biological sensitivity.
12031-89
Author(s): Dominik Eckert, Ludwig Ritschl, Magdalena Herbst, Julia Wicklein, Siemens Healthineers (Germany); Sulaiman Vesal, Stanford Univ. (United States); Steffen Kappler, Siemens Healthineers (Germany); Andreas Maier, Friedrich-Alexander Univ. (Germany); Sebastian Stober, Otto-von-Guericke Univ. Magdeburg (Germany)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this study, the possibility of using deep learning for noise removal on mammographic x-ray images is investigated. Moreover, we show that loss functions have a significant impact on the detail preservation in deep learning based x-ray denoising. As a result of this investigation, a novel loss function specialized for mammogram denoising is proposed. To do so, a physics driven data augmentation chain is used, which simulates a radiation dose reduction. Deep convolutional neural networks are then trained with different loss functions. Their denoising performance is then analyzed on the preservation of medically relevant details.
12031-101
Author(s): Brian P. Toner, The Univ. of Arizona (United States); Andrey Makeev, Prabhat KC, Stephen Glick, U.S. Food and Drug Administration (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional (3D) imaging modality that uses limited angle tomography. DBT typically exhibits high in-plane resolution, with poor out-of-plane resolution. This out-of-plane blur in DBT distorts the reconstructed lesion and can degrade lesion quantification and volume estimation. Neural networks can be trained to predict a full angle CT reconstruction from a limited angle DBT input image. A network was trained to perform this image restoration using a large number of Monte Carlo simulated lesion volumes-of-interest (VOI) from DBT and breast CT reconstructions. Using the output images of the trained neural networks will hopefully allow for more accurate lesion quantification and volume estimation than is possible with DBT images.
12031-105
Author(s): Rodrigo T. Massera, Alessandra Tomal, Univ. of Campinas (Brazil)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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This work studies the performance of a previously developed deep learning framework, trained with virtual breast phantoms, for volumetric breast density (VBD) estimation applied to clinical digital mammography images. We compared the predicted VBD of 199 real images with the results obtained from Volpara software (Volpara Health, New Zealand, v. 1.5.1). The coefficient of determination (r²) was 0.72. Moreover, the median (10%-90% intervals) VBD intervals were 8.6%(4.0%-21.1%) and 6.9%(3.1%-19.4%) using Volpara and DFL, respectively. In conclusion, a good agreement between both approaches was achieved.
12031-120
Author(s): Bahaa Ghammraoui, Stephen J. Glick, Shahed Bader, U.S. Food and Drug Administration (United States); Thomas Thuering, DECTRIS Ltd. (Switzerland)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The purpose of this study was to investigate the use of a Gallium Arsenide (GaAs) photon-counting spectral mammography system to differentiate between Type I and Type II calcifications. Type I calcifications, consisting of calcium oxalate dihydrate (CO) or weddellite compounds are more often associated with benign lesions, and Type II calcifications containing hydroxyapatite (HA) are associated with benign or malignant lesions. The study was carried out on a custom-built laboratory bench-top system using the SANTIS 0804 GaAs detector prototype system from DECTRIS Ltd. Measurements were performed on CIRS (Norfolk, VA) swirl and uniform phantoms mimicking a 50% adipose, 50% fibroglandular breast tissue composition with inserted clusters of synthetic microcalcifications. First, an inverse problem-based approach was used to estimate the full energy x-ray transmission fraction factor using known basis transmission factors of varying thicknesses of Aluminum and PMMA at each pixel. Secon
12031-123
Author(s): Rodrigo de Barros Vimieiro, Lucas Rodrigues Borges, Univ. de São Paulo (Brazil); Bruno Barufaldi, Andrew D. A. Maidment, Univ. of Pennsylvania (United States); Ge Wang, Rensselaer Polytechnic Institute (United States); Marcelo Andrade da Costa Vieira, Univ. de São Paulo (Brazil)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Convolutional neural networks (CNNs) have been used for image processing tasks such as denoising, super-resolution, deblurring, etc. It is known that deep models require a large amount of data for the training process. In the medical imaging field, data availability is a concern due to patient privacy and radiation exposure. In this work, we investigated the effect of the dataset size in the performance of deep neural network, based on the residual network architecture, dedicated to restore low-dose digital breast tomosynthesis (DBT) raw projections. We generated 500 synthetic breast phantoms through a virtual clinical trials (OpenVCT) software and validated them in terms of noise and signal properties. We generated raw DBT projections considering a commercially available DBT system and restored the low-dose (LD) projections after training the CNN with different dataset sizes. Our goal is to restore the LD projections to achieve the same characteristics as the full-dose projections.
12031-137
Author(s): Vincent Dong, Tristan D. Maidment, Real-Time Tomography, LLC (United States); Lucas R. Borges, Real-Time Tomography LLC (United States); Bruno Barufaldi, Univ. of Pennsylvania (United States); Susan Ng, Real-Time Tomography, LLC (United States); Andrew D. A. Maidment, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Noiseless digital mammograms (DM) are unobtainable in clinical screening environments, limiting the development of deep learning-based (DL) denoising applications. Virtual clinical trials (VCTs) allow the precise simulation of noise levels in DM images for controlled training of DL models. We evaluated a set of DL denoising models, trained using VCT data, that showcases the trade-offs between denoising strength and fine structure preservation. Our results show that metrics, such as peak signal-to-noise ratio (PSNR), are improved with the use of our trained residual convolutional neural network. This quantifiable improvement indicates that our proposed DL methodology can accurately denoise simulated mammograms.
12031-145
Author(s): Diksha Sharma, Kenny H. Cha, U.S. Food and Drug Administration (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We compare simulated mammograms with real mammogram images using Radiomics texture features. Simulated mammogram images are from VICTRE, an in silico breast imaging clinical trial toolbox. CBIS-DDSM dataset is used for the real mammograms. Texture features are calculated on the entire breast to compare the overall texture. Our results indicate that VICTRE can be seen as a subset of the CBIS-DDSM dataset in terms of textural variability. This work shows that Radiomics can be a useful tool for comparing breast characteristics between different datasets, including simulated versus real datasets.
12031-155
Author(s): Bryce Smith, Joyoni Dey, Louisiana State Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The objective of this work was to create a MLEM software-based scatter correction algorithm for removing the effect of Compton Scatter from mammography images acquired without scatter grid or with analyzer-less interferometry. We developed an MLEM algorithm with an efficient linear scatter model to estimate the thickness of compressed breast and evaluated the algorithm with breast images acquired with the GEANT4 Monte Carlo software. The thicknesses estimated from the algorithm on the GEANT4 images were compared to the true geometric thicknesses of the ellipsoid for each pixel of the detector and matched to within ~2mm RMS error.
12031-156
Author(s): Hanna Tomic, Daniel Förnvik, Sophia Zackrisson, Anders Tingberg, Magnus Dustler, Skåne Univ. Hospital (Sweden), Lund Univ. (Sweden); Predrag Bakic, Penn Medicine, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Virtual clinical trials (VCTs) in medical imaging can be used as a supplement to clinical trials. This study aims at modeling the variety in volumetric breast density (VBD) in a virtual population of women attending mammography screening. Probability distributions (PDs) were fitted to available clinical data on VBD from Malmö, for women in different age groups. The PDs were then used for sampling VBD values for virtual populations. Results showed no significant difference in VBD values between the clinical data and virtual populations. The study suggests that the method could be applied when simulating VBD for virtual patients in VCTs.
Posters: Cone-Beam CT
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-80
Author(s): Joakim da Silva, Elekta AB (Sweden); Maxin Chen, Elekta Beijing Medical Systems Co. Ltd. (China); Markus Eriksson, Elekta AB (Sweden)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We present a novel geometric calibration phantom and method for producing gantry-angle-dependent, nine-degrees-of-freedom calibrations of CBCT systems. The phantom does not require costly manufacturing but relies on 2D patterns using common circuit-board technology. Relative pattern positions are determined as part of the calibration, offering robustness to errors in phantom assembly and changes in geometry over time. Further, the method is developed particularly for robust calibration of systems with a highly offset detector, ensuring calibration patterns always cover a large portion of the detector and that the same patterns are visible in opposing projections by extending these across the overlap region.
12031-92
Author(s): Zhilei Wang, Hao Zhou, Shan Gu, Binxiang Qi, Hewei Gao, Tsinghua Univ. (China)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-103
Author(s): Wooseob Kim, Kyung Hee Univ. (Korea, Republic of); Jaeik Jung, CAT Beam Tech Co., Ltd. (Korea, Republic of); Jeungsun Ahn, Jehwang Ryu, Kyung Hee Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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X-ray system for computed tomography scanning based on carbon nanotube cold cathode were constructed. The x-ray images were acquired through pulsed voltage using field effect transistor (FET) circuit. The obtained x-ray images were reconstructed into tomography images using back projection and filtered back projection algorithms. The resulting reconstructed x-ray image clearly shows micrometer scale.
12031-106
Author(s): Kihong Son, Electronics and Telecommunications Research Institute (Korea, Republic of); Hyoeun Kim, Electronics and Telecommunications Research Institute (Korea, Republic of), Hanyang Univ. (Korea, Republic of); Yurim Jang, Electronics and Telecommunications Research Institute (Korea, Republic of), Ajou Univ. (Korea, Republic of); Seunghoon Chae, Sooyeul Lee, Electronics and Telecommunications Research Institute (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We developed a region-of-interest (ROI) image reconstruction method that effectively reduces truncation artifacts in CBCT. By using U-Net-based deep learning (DL) methods, we devised a method to reduce truncation artifacts for ROI imaging. A total of 16294 image slices from 49 patient cases were used to generate projection data. The center of the projected image was cropped to a width of 150 mm. Then, the outer part of the truncation image was filled with each outermost pixel value for the initial correction. After the filtering process, the truncation area was cut off and used as input data in the DL model. Finally, inference images were reconstructed by use of the FDK algorithm. SSIM values for the test set of 14 patients were calculated as 0.541, 0.709 and 0.979 for FBP, Extension and the proposed ROI method, respectively. We have achieved promising results and believe that the proposed ROI image reconstruction method can help reduce radiation dose while preserving image quality.
12031-116
Author(s): Jiongtao Zhu, Ting Su, Jiecheng Yang, Dong Liang, Yongshuai Ge, Shenzhen Institutes of Advanced Technology (China)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this work, we propose an innovative deep learning method to realize three-material decomposition from the dual-energy CBCT scans. By design, this dedicated end-to-end CNN network accepts the low and high energy CBCT projections, and automatically outputs three different basis image volumes (water basis, iodine basis, CaCl2 basis). Experimental phantom with ground truth was scanned, and results demonstrate that high accurate decomposition errors (less than 5%) can be achieved. In conclusion, CNN based multi-material (≥3) decomposition approach shows promising benefits for high quality dual-energy CBCT imaging applications.
12031-126
Author(s): Juan Pablo Cruz-Bastida, Erik Pearson, Hania Al-Hallaq, The Univ. of Chicago (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The scanning protocol used for CBCT acquisitions is largely determined by the anatomical location of the procedure. Automatically identifying the anatomical location from a single x-ray projection could help ensure consistent image quality and proper patient exposure. A deep learning model was developed for the auto-classification of anatomical regions from single x-ray projections. A VGG16 CNN was able to determine the anatomical with accuracy over 91% for the head, neck and thorax, and over 82% for pelvis and abdomen. Classification performance plateaued at dataset sizes above 750 images. There was not a substantial change in performance vs angle across classes.
12031-135
Author(s): Boyuan Li, Derrek Spronk, Yueting Luo, Connor Puett, Christy Inscoe, Don A. Tyndall, Yueh Z. Lee, Jianping Lu, Otto Z. Zhou, The Univ. of North Carolina at Chapel Hill (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-140
Author(s): Hana Haseljić, Robert Frysch, Vojtěch Kulvait, Tim Pfeiffer, STIMULATE, Otto-von-Guericke Univ. Magdeburg (Germany); Bennet Hensen, Frank Wacker, Inga Brüsch, Thomas Werncke, Medizinische Hochschule Hannover (Germany); Georg Rose, Otto-von-Guericke-Univ. Magdeburg (Germany)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We investigate the impact of tube voltage modulation, (TVM), in a C-arm cone-beam CT perfusion imaging setting. We conduct a simulation study based on a real perfusion acquisition to compare results from acquisitions with and without modulation. Using two different reconstruction techniques, we analyze the influence of TVM on the perfusion parameters. The high correlation (r > 0.996) between the results with and without TVM are achieved for all perfusion parameters using a model-based reconstruction technique. These findings suggest that dose modulation techniques, incl. TVM, can be used in C-arm CT perfusion scans without the need for additional correction methods.
12031-150
Author(s): Daniel Punzet, Otto-von-Guericke Univ. Magdeburg (Germany), Research Campus STIMULATE (Germany); Robert Frysch, Otto-von-Guericke-Univ. Magdeburg (Germany), Research Campus STIMULATE (Germany); Daniel Behme, Univ. Hospital Magdeburg (Germany), Research Campus STIMULATE (Germany); Tim Pfeiffer, Oliver Speck, Otto-von-Guericke Univ. Magdeburg (Germany), Research Campus STIMULATE (Germany); Georg Rose, Otto-von-Guericke-Univ. Magdeburg (Germany), Research Campus STIMULATE (Germany)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In many interventional settings it would be beneficial to perform a final CBCT acquisition for outcome control after the intervention is done. However, due to high patient dose this is often omitted. Volume-of-interest acquisitions offer considerable dose reduction, but image reconstruction typically suffers from cupping artifacts and offsets in radiodensity due to the truncated projection data. In a previous work we presented a method which allows to incorporate available prior volume data into the reconstruction of volume-of-interest acquisitions in CBCT. The method works by making use of the fluoroscopic positioning images typically acquired before CBCT acquisitions in a 3D Radon space-based registration method registering the prior volume to the volume-of-interest scenario. Here, we demonstrate the application of this method on real clinical data or the first time.
12031-157
Author(s): Qian Wang, Emory University (United States); Huiqiao Xie, Tonghe Wang, Justin Roper, Xiangyang Tang, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang, Emory Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Cone-beam CT (CBCT) plays a crucial role in modern image-guided radiotherapy for patient setup and verification. However, the image quality of CBCT is inferior to that of CT in terms of HU accuracy, image artifact, and tissue contrast, which impedes the potential of CBCT in further radiotherapy applications, such as online contouring and dose calculation for adaptive radiotherapy. In this work, we propose an optimization model for material decomposition of spectral CBCT, which innovatively incorporates CT as guidance to simultaneously improve the image quality and material composition of CBCT images. Both phantom and patient studies demonstrate the effectiveness and superiority of the proposed method in noise and artifact removal, decomposition-accuracy maintenance, etc.
12031-164
Author(s): Samaa Dweek, Salam Dhou, Tamer Shanableh, American Univ. of Sharjah (United Arab Emirates)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
Posters: CT and Multi-Energy CT
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-81
Author(s): Zhongxing Zhou, Nathan R. Huber, Akitoshi Inoue, McCollough H. Cynthia, Lifeng Yu, Mayo Clinic (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-93
Author(s): Sachin S. Shankar, Duke Univ. (United States); Eric A. Hoffman, Jarron Atha, Jessica C. Sieren, The Univ. of Iowa (United States); Ehsan Samei, Ehsan Abadi, Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Quantifications derived from CT images should accurately reflect patient conditions, but in practice scanner and imaging parameters can affect these quantifications. The purpose of this study was to evaluate variability in CT quantifications caused by these parameters through lung imaging quantifications. A COPD-emulated anthropomorphic chest phantom (Kyoto Kagaku) was used with 8 scanners (GE, Siemens, Philips) and the images were analyzed in terms of seven clinically-relevant lung biomarkers. Analysis showed that both intra and inter-scanner comparisons have effects on variability. These findings demonstrate the need of further research on how to harmonize CT images for more accurate quantifications.
12031-100
Author(s): Mridul Bhattarai, Duke Univ. Medical Physics Graduate Program (United States); Shobhit Sharma, Duke Univ. (United States); Stevan Vrbaški, Univ. degli Studi di Trieste (Italy), Istituto Nazionale di Fisica Nucleare (Italy); Ehsan Abadi, Duke Univ. School of Medicine (United States), Duke Univ. (United States); William P. Segars, Duke Univ. School of Medicine (United States); Adriano Contillo, Elettra-Sincrotrone Trieste S.C.p.A. (Italy); Renata Longo, Univ. degli Studi di Trieste (Italy), Istituto Nazionale di Fisica Nucleare (Italy); Ehsan Samei, Duke Univ. School of Medicine (United States), Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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This study develops a validated modular detector response model for studying the combined effects of charge sharing and pulse pileup on image acquisition in CdTe- and Si-based photon-counting CT (PCCT). The model combines Monte Carlo simulations with analytical models for charge sharing and pulse pileup to simulate spectral distortion inherent to signal collection in photon-counting detectors. The model is also integrated with an existing imaging simulation platform (DukeSim) for conducting virtual imaging trials, enabling the effective evaluation and optimization of a wide variety of existing and upcoming PCCT technologies.
12031-107
Author(s): Angela Li, Amherst College (United States), Univ. of Pennsylvania (United States); Peter Noël, Nadav Shapira, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Bolus tracking can optimize the time delay between contrast injection and diagnostic scan initiation in contrast-enhanced CT. However, this procedure is time consuming and subject to inter-operator variance. The purpose of this study is to apply a machine learning approach to automate and standardize locator scan positioning for bolus tracking applications. In our models, we adopted a transfer learning strategy to improve sample efficiency and added a fully connected regression layer to predict the desired locator scan position. We evaluated the performance of various model architectures, and our results demonstrate the potential to standardize the bolus tracking workflow.
12031-118
Author(s): Derrek Spronk, Yueting Luo, Alex Billingsley, Christina R. Inscoe, Otto Zhou, Jianping Lu, Yueh Z. Lee, The Univ. of North Carolina at Chapel Hill (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Computed Tomography (CT) is a three dimensional imaging modality in which x-ray projections are acquired from viewpoints around the object. By replacing the rotating source with a stationary array of carbon nanotube (CNT) enabled X-ray sources, a specialized head CT scanner has been constructed with the aim of generating diagnostic quality images without source or detector motion. The current study demonstrates that CNT x-ray source arrays can be practically used for stationary head CT imaging. Here we present a prototype s-HCT scanner which can generate volumetric phantom images with continuous motion of the imaging object along the system z-axis.
12031-124
Author(s): Nimu Yuan, Univ. of California, Davis (United States); Jian Zhou, Canon Medical Research USA, INC. (United States); Jinyi Qi, Univ. of California, Davis (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this study, we proposed a deep learning-based approach for sparse-view CT spatial resolution enhancement. The proposed method utilizes a densely connected convolutional neural network that is further aided by a radial location map to recover the radially dependent blurring caused by the continuous rotation of the x-ray source. The results showed that the proposed method was able to recover the resolution loss and improve the image quality. Compared with the network using the same main structure but without a radial location map, the proposed method achieved better image quality in terms of the mean absolute error and structure similarity.
12031-132
Author(s): Sjoerd Tunissen, Luuk J. Oostveen, Nikita Moriakov, Jonas Teuwen, Koen Michielsen, Ewoud J. Smit, Ioannis Sechopoulos, Radboud Univ. Medical Ctr. (Netherlands)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We present a CT simulator capable of generating scanner-specific sinograms of digital phantoms. Noise and resolution properties were characterized on a clinical CT system and used to apply appropriate resolution loss and noise texture to polychromatic raytraced projections of digital phantoms. Simulator results are compared to acquisitions by the clinical CT system, showing an average difference of 2.1% in the off-center MTF, 0.048 in normalized NPS and a 6 HU difference in the value for water. This simulator can provide data with known ground truth and various acquisition parameters, making it ideal for developing and testing image processing algorithms.
12031-147
Author(s): Thomas W. Holmes, Emory Univ. (United States); Nadav Shapira, Univ. of Pennsylvania (United States); Nariman Nezami, Univ. of Maryland (United States); Mahdiyeh Ghorbani-Zavareh, Siemens Healthcare (Germany); Shiu Kai Fung, Siemens Healthcare (United States); Bernhard Schmidt, Siemens Healthcare (Germany); Carlo N. De Cecco, Emory Univ. (United States); Peter Noel, Univ. of Pennsylvania (United States); Amir Pourmorteza, Winship Cancer Institute of Emory Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We investigated the image quality performance of a novel dual-source photon-counting detector (PCD) CT scanner for spectral and ultra-high-resolution (UHR) imaging of coronary stents. PCD-CT in UHR mode significantly improved stent lumen visualization for stent diameters as low as 2mm.
12031-154
Author(s): Chih-Wei Chang, Yuan Gao, Qian Wang, Yang Lei, Tonghe Wang, Jun Zhou, Liyong Lin, Jeffrey D. Bradley, Tian Liu, Xiaofeng Yang, Emory Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-161
Author(s): Saman Sotoudeh-Paima, W. Paul Segars, Ehsan Samei, Ehsan Abadi, Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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CT imaging has proved to be an effective tool in evaluating COPD patients. The development of photon-counting detectors has offered better spatial resolution and reduced image noise, overcoming one major limitation of conventional scanners. However, this promising technology needs to be compared against conventional scanners and carefully optimized for its specific application. The purpose of this study was to investigate the potential benefits of PCCT in the context of COPD imaging. Our analysis demonstrated that PCCT achieved more accurate results compared to conventional scanners and that sharp kernels and thicker reconstruction slices would negatively affect the accuracy.
12031-165
Author(s): Alexander A. Zamyatin, Leiming Yu, David Rozas, Analogic Corp. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We present a novel 3D RED-CNN architecture for edge-preserving image noise reduction in CT images, evaluate effect of model parameters on performance and image quality and show steps to improve optimization convergence. We use standard imaging metrics (SSIM, PSNR) to assess imaging performance and compare to previously published algorithms. Compared to 2D RED-CNN, our proposed 3D RED CNN produces higher quality 3D results, as shown by reformatted (sagittal, coronal) views, while maintaining all advantages of the 2D RED-CNN in axial imaging.
12031-170
Author(s): Anna Marie Gann, Duke Univ (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this study, we calculate the minimum detectable change (𝐷𝑚𝑖𝑛) in 21 radiomics features across CT imaging protocols and acquisition settings to understand their influence on the diagnostic quality of CT images. We consider lung nodules of four diameters, 3 dose conditions and slice thicknesses. We find that protocols specific to the Quantitative Imaging Biomarker Alliance (QIBA) perform well across nodule sizes and score reduced variability overall compared to other protocols tested. We find that feature variability decreases with nodule diameter, and that size-based features are more variable, while shape-based features are relatively more stable across protocols.
12031-171
Author(s): Eri Haneda, Bruno De Man, Bernhard Claus, Lin Fu, GE Global Research (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The Zoom-In Partial Scans (ZIPS) method is a recently introduced high-resolution CT technique that utilizes the high geometric magnification of off-center imaging regions to boost the intrinsic resolution of clinical multi-slice CT. ZIPS performs two partial off-center scans to acquire high-resolution CT data of a local region of interest (ROI), then the two partial scans are merged during image reconstruction. In this study, we illustrate the feasibility of ZIPS CT reconstruction with simultaneous compensation of inter-scan rigid motion of the ROI between the two partial scans, which leads to a clear improvement of spatial resolution when compared with standard CT.
12031-174
Author(s): Thomas Holmes, Roozbeh Tarighati Rasekhi, Sara Shirazi, Erica Riedesel, Emory Univ. School of Medicine (United States); Amir Pourmorteza, Emory Univ. School of Medicine (United States), Georgia Institute of Technology (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Patient specific dose estimation is traditionally calculated though Monte-Carlo methods but not performed during CT image acquisition due to long computation times. We propose the implementation of a NN to perform patient specific dose estimation that can be performed alongside the CT acquisition due to the reduction in computation time. This is achieved by first performing MC simulations and then training the NN to replicate these predictions. The NN shows promise with a high degree of total cranial dose accuracy between the predictions and ground truth with a standard deviation of less than ±0.5 mGy.
12031-177
Author(s): Sunho Lim, Gyuseong Cho, Seungryong Cho, KAIST (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Low-dose CT has been investigated and employed in various forms for clinical practices. One of the viable options for low-dose imaging is using a multi-slit beam collimator to achieve a sparse sampling. We have earlier demonstrated the feasibility of such technique, and extended the method for dual-energy imaging from a single scan using a multi-slit beam-filter in the circular CBCT system. Multi-detector-row helical CT is indeed in wide use in the clinics. In this work, we continue to explore the beam-filter based imaging technique in the multi-row helical CT scans. We conducted a simulation study by applying a virtual filter to the real data acquired from a helical CT scanner. We separated the sinograms of each row from the multi-row helical CT data, and removed the streaks using a notch filter in the Fourier domain of each sinogram. We then reconstructed the image by use of the filtered-backprojection algorithm and reduced the image noise by applying l0-norm based smoothing.
12031-178
Author(s): Alex Billingsley, Derek Spronk, Yueting Luo, Christy R. Inscoe, Otto Zhou, Jianping Lu, Yueh Z. Lee, The Univ. of North Carolina at Chapel Hill (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Purpose: The purpose of this study is to explore calibration techniques for stationary CT scanners using simulations. Method: The direct linear transform (DLT) is used to generate a starting calibration for subsequent optimization. The phantom design was optimized through simulation of the DLT calibration method. The initial calibration was refined using point based reprojection optimization. Results: Calibration methods in simulation proved successful and capable of achieving desired geometric precision and accuracy. Most parameters are determined to within 0.6mm or 1 degree. Conclusion: Simulations with this approach demonstrate that an accurate calibration can be performed for the stationary Head CT system using the DLT method.
12031-181
Author(s): Takayuki Okamoto, Hideaki Haneishi, Chiba Univ. (Japan)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Sparse-view computed tomography enables low-dose radiation and short-time acquisition by reducing the number of projections. However, the reconstructed images in sparse-view suffer from severe streak artifacts associated with back-projection. In this paper, we proposed a new correction method for sparse-view sinograms with frequency domain information. We introduced a weighted frequency domain loss function that minimizes the absolute differences of the frequency components between sparse-view sinogram and fully sampled sinogram. Experimental results show that a fully convolutional network with the proposed loss function was superior to the one without it qualitatively and quantitatively. We confirmed the effectiveness of the proposed method.
12031-182
Author(s): Byeongjoon Kim, Hyunjung Shim, Jongduk Baek, Yonsei Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Because streak artifacts in a sparse-view computed tomography (CT) image are deterministic errors, we regenerate the streak artifacts by forward and back-projection of a prior image, and then subtract them from a sparse-view image. To obtain a patient-specific prior image, we introduce implicit neural representations in which neural networks output a pixel value for an input pixel coordinate. The implicit neural representation is optimized with sparse-view projection data of a single patient. We validated the proposed method using fan-beam CT simulation data of an extended cardiac-torso phantom and compared the results with total variation-based iterative reconstruction and an image-based convolutional neural network.
12031-184
Author(s): Tzu-Cheng Lee, Jian Zhou, Zhou Yu, Liang Cai, Canon Medical Research USA, Inc. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Wide-coverage detector CT and ultra-high-resolution (UHR) detector CT are two important features for current cardiac imaging modalities. However, no commercially available scanner has both these features in one scanner as of today. We propose to use existing UHR-CT data to train a super resolution (SR) neural network and apply the network in a wide-coverage detector CT system. The purpose of the network is to enhance the system resolution performance and reduce the noise while maintaining its wide-coverage feature without additional hardware changes. The MTF measured from Catphan phantom scans showed the proposed super-resolution aided deep learning-based reconstruction improved the MTF resolution by relative ~30% and ~10% as compared to filtered-back projection and model-based iterative reconstruction approaches. In real patient cases, the SR-DLR images also show better noise texture and enhanced spatial resolution compared to other reconstruction approaches.
12031-187
Author(s): Fakrul Islam Tushar, Duke Univ. (United States), Duke Univ. School of Medicine (United States); Husam Nujaim, Univ. de Girona (Spain); Wanyi Fu, Duke Univ. (United States); Ehsan Abadi, Maciej A. Mazurowski, William P. Segars, Ehsan Samei, Joseph Y. Lo, Duke Univ. (United States), Duke Univ. School of Medicine (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-188
Author(s): Jayasai R. Rajagopal, Duke Univ. (United States), National Institutes of Health Clinical Ctr. (United States); Cindy McCabe, Giavanna L. Jadick, Ehsan Abadi, Justin B. Solomon, Duke Univ. (United States); Pooyan Sahbaee, Juan-Carlos Ramirez Giraldo, Siemens Healthineers (United States); Paul Segars, Duke Univ. (United States); Elizabeth C. Jones, National Institutes of Health Clinical Ctr. (United States); Ehsan Samei, Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this work, we aimed to characterize and optimize a prototype dual-source photon-counting CT (PCCT) system (NAEOTOM Alpha, Siemens) system for abdominal and thoracic imaging protocols. Using this system, we scanned a physical phantom (Mercury, Sun Nuclear) with two subsections, a uniform region for noise characterization and a section with inserts for task-based assessment. Additionally, simulated images of virtual patient models (XCAT) with pathologies were generated using a scanner-specific CT simulation platform (DukeSim, Duke). Images were reconstructed with parameters specific for abdominal and thoracic imaging protocols. The results noted enhanced contrast and low noise for abdominal protocols and high resolution for thoracic protocols. Provided with added information available from the scanner, the results noted the importance for close balance between pixel and slice geometry, kernel, and applied dose, to enhance the clinical utility of the system.
12031-191
Author(s): Ting Su, Jiongtao Zhu, Jiecheng Yang, Dong Liang, Yongshuai Ge, Shenzhen Institutes of Advanced Technology (China)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this abstract, a low-cost spectral CT data acquisition protocol and multi-material image reconstruction algorithm are proposed. By slowly modulating the X-ray tube voltage over the gantry rotation, angularly varied spectral information can be acquired within one single CT scan. With the dedicated model-based one-step material decomposition algorithm, at least three basis images can be obtained. A numerical phantom with ground truth (iodine and gadolinium) is simulated at different kVp modulations with varied periods. Results demonstrate that the proposed approach is able to accurately decompose the water, iodine and gadolinium basis.
12031-192
Author(s): Raziye Kubra Kumrular, Thomas Blumensath, Univ. of Southampton (United Kingdom)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We proposed the optimization of a more generic X-ray tomography model. By imaging an object with a range of source spectra, we showed that spectral images could nevertheless be obtained, even if we have energy blind detectors. For observations to be integrated over energy levels, we tried to solve the problem by jointly estimating the X-ray measurement and X-ray absorption spectrum under the constraint. The research includes using DL (deep-learning) to learn a low-dimensional model of absorption spectra, using DL constraint to recover absorption spectra from X-ray measurements, and using DL to decompose absorption spectra estimates into material density estimates.
12031-195
Author(s): Hannes Karlsson, Viggo Moro, Alma Eguizabal, Morris Eriksson, Adam Ågren, Dennis Åkerström, Mats U. Persson, KTH Royal Institute of Technology (Sweden)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Photon-counting CT allows for material decomposition, which decomposes the imaged object into a set of basis materials. We study the use of deep convolutional neural networks to improve image quality of basis images resulting from three-material decomposition. We compare the U-Net, Dilated U-Net and ResNet architectures, each applied in either the image domain or in the projection domain. We evaluate these in terms of contrast-to-noise ratio, task transfer function and noise power spectrum. Preliminary results show that the most promising option is to use either the U-Net or Dilated U-Net architectures and that sinogram-domain networks generalize better to unseen data.
12031-196
Author(s): Byeongjoon Kim, Seunghyuk Moon, Yunsu Choi, Jongduk Baek, Yonsei Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this study, we aim to initially reduce motion artifacts using sparse view CT with a fast scan mode, and to reduce both the streak and motion artifacts using a CNN-based two-phase approach. In the first phase, we employed the U-net architecture and residual learning scheme to effectively reduce streak artifacts in the presence of motion artifacts. In the second phase, we compensated motion artifacts using the attention blocks with global average pooling. The proposed two-phase approach effectively reduced both the motion and streak artifacts and taking fewer projection views down to one-eighth views (thus improving a scanning speed) provided the better image quality in our simulation study.
12031-198
Author(s): Njood Alsaihati, Justin Solomon, Ehsan Samei, Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-200
Author(s): Francesco Ria, Giavanna L. Jadick, Ehsan Abadi, Justin B. Solomon, Ehsan Samei, Duke Univ. Health System (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Several methods have been introduced over the years to measure CT image noise magnitudes in vivo applying different image segmentation strategies, HU thresholds, and regions of interests in which the noise is calculated. Therefore, it is impossible to directly compare results obtained with different methods and to objectively evaluate image quality in CT. This study provided an unbiased comparison of two methods to estimate noise utilizing a Virtual Imaging Trials platform simulating 1800 image datasets representing different dose levels, anatomical areas, and reconstruction techniques. Only methods that measure noise magnitude in soft tissue can effectively inform protocol design and optimization.
12031-201
Author(s): Andrew Lin, Nipun Manral, Priscilla McElhinney, Aditya Killekar, Cedars-Sinai Medical Ctr. (United States); Hidenari Matsumoto, Showa Univ. (Japan); Jacek Kwiecinski, Konrad Pieszko, Aryabod Razipour, Kajetan Grodecki, Caroline Park, Mhairi Doris, Alan Kwan, Donghee Han, Keiichiro Kuronama, Guadalupe Flores Tomasino, Evangelos Tzolos, Aakash Shanbhag, Cedars-Sinai Medical Ctr. (United States); Markus Goeller, Cedars-Sinai Medical Ctr. (United States), Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany); Mohamed Marwan, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany); Sebastien Cadet, Cedars-Sinai Medical Ctr. (United States); Stephan Achenbach, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany); Stephen Nicholls, Monash Health (Australia), Monash Univ. (Australia); Dennis Wong, Monash Cardiovascular Research Ctr. (Australia); Daniel Berman, Cedars-Sinai Medical Ctr. (United States); Marc Dweck, David Newby, Michelle E. Williams, The Univ. of Edinburgh (United Kingdom); Piotr Slomka, Damini Dey, Cedars-Sinai Medical Ctr. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-202
Author(s): Xiaohui Zhan, Ruoqiao Zhang, Brent Budden, Xiaofeng Niu, Ilmar Hein, Yi Qiang, Zhou Yu, Richard Thompson, Canon Medical Research USA, Inc. (United States); Hiroaki Nakai, Hiroaki Miyazaki, Canon Medical Systems Corp. (Japan)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Recent studies have demonstrated that semiconductor-based photon counting CT (PCCT) has the potential to provide better contrast and noise performance compared to conventional scintillator-based systems. With multi-energy threshold detection, it can also provide additional spectral information and enable material decomposition to better differentiate different materials. In this work, we introduce our first CdZnTe-based full size photon counting CT system, and report some initial phantom imaging studies at clinical dose levels.
12031-203
Author(s): Keisuke Yamakawa, Taiga Goto, Masatoshi Kudo, Yuko Aoki, FUJIFILM Healthcare Corp. (Japan)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We have developed new Artificial Intelligence Metal Artifact Reduction (AI-MAR) to directly predict metal artifacts using deep learning and subtract them from the original Filtered Back-Projection (FBP) image to maintain the structure of the object and achieve high artifact reduction effect. In a metal simulation experiment, the proposed AI-MAR successfully reduced the metal artifacts and improved Structural Similarity Index compared with FBP, linear interpolation method, and conventional MAR. The proposed AI-MAR can improve device performance by providing high-speed and highly accurate images with reduced metal artifacts.
12031-204
Author(s): Connor J. Evans, Univ. of Notre Dame (United States); Mengzhou Li, Chuang Niu, Ge Wang, Rensselaer Polytechnic Institute (United States); Ryan K. Roeder, Univ. of Notre Dame (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Material decomposition (MD) algorithms enable discrimination and quantification of multiple contrast agent and tissue compositions in spectral image datasets acquired by photon-counting computed tomography (PCCT). Reduction of image artifacts and noise could further improve the accuracy of MD but the effects of image denoising on MD have not been investigated. Therefore, MD was performed on as-acquired and denoised PCCT image datasets comprising multiple contrast agent and tissue compositions of known concentration. Image denoising using either deep learning or the BM3D algorithm improved the quantitative accuracy of MD, but deep learning preserved local image detail compared to BM3D.
12031-205
Author(s): Junbo Peng, Georgia Institute of Technology (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Compared with commercial dual-energy computed tomography (DECT) solutions, beam modulation is an economical and efficient alternative to achieve single-scan DECT which can be enabled with conventional diagnostic or cone-beam CT machines. However, existing beam modulation methods usually require a moving block scheme and cannot handle the half-fan protocol due to inevitable data deficiency. In this work, a dedicated beam modulator is placed on the detector surface to split the projected photons into high- and low-energy parts, then the mixed-spectral sinogram is fed into a pre-trained pix2pix GAN model to generate full-sampled sinograms at different spectra and analytical reconstruction and material decomposition can be subsequently implemented using the generated sinogram data.
12031-206
Author(s): Shinichi Kojima, Kazuma Yokoi, Isao Takahashi, FUJIFILM Healthcare Corp. (Japan)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Photon Counting Detector CT images utilizing energy information sometimes show band artifacts of not only dark but also bright bands due to finite energy widths of the detector and so on. To correct these artifacts, the method combining a conventional beam hardening correction and information derived from virtual monochromatic images created with material decomposition images was developed. The proposed method was demonstrated with phantom data acquired by FUJIFILM’s prototype machine and found that artifacts can be successfully reduced.
12031-207
Author(s): SeungWon Choi, Jongduk Baek, Yonsei Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In helical scan mode, direct filtered backprojection method introduces helical artifacts in the reconstructed volume. Therefore, this study proposed a method to reduce the helical artifacts in the reconstructed volume. The proposed algorithm proceeds in two sequential parts: bone induced artifacts reduction part and soft tissue induced artifacts reduction part. The proposed algorithm was verified for extended cardiac-torso (XCAT) simulation. Performance of the proposed algorithm was quantitatively evaluated by normalized mean square error (NMSE) and structure similarity index (SSIM). The results showed that the proposed algorithm can reduce helical artifacts effectively.
12031-208
Author(s): Stevan Vrbaski, Renata Longo, Univ. degli Studi di Trieste (Italy); Adriano Contillo, Elettra-Sincrotrone Trieste S.C.p.A. (Italy)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this work, we applied the singular value decomposition method to a set of spectral images to extract the dominant physical contributions to image formation. Through the theoretically derived method, we showed that the first two principal components can be related to traditional pair of basis materials. The study relied on both numerical and physical spectral CT phantom images obtained with monochromatic x-ray radiation at Elettra synchrotron in Trieste, Italy. Following material decomposition, we also performed a quantitative description of tissue-equivalent phantom materials in terms of material density and effective atomic number.
12031-209
Author(s): Vojtech Kulvait, Otto-von-Guericke Univ. Magdeburg (Germany); Philip Hoelter, Universitätsklinikum Erlangen (Germany), Friedrich-Alexander-Univ. Erlangen Nürnberg (Germany); Arnd Doerfler, Universitätsklinikum Erlangen (Germany), Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany); Georg Rose, Otto-von-Guericke-Univ. Magdeburg (Germany)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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CT perfusion imaging (CTP) is an important modality for assessing the severity of ischemic stroke. The CTP data acquisition time involving exposure to ionizing radiation is approximately one minute. Low-dose protocols lead to increased noise in the produced CT images and perfusion maps. We present a method that reduces noise in the data by reducing the dimension of time attenuation curves. To reduce the dimension, we use the first few terms of the trigonometric polynomial or SVD decomposition. Both methods lead to significant noise reduction and preserve important information in the resulting perfusion maps.
Posters: Detectors
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-85
Author(s): Tristen T. Thibault, Oleksandr Grynko, Emma Pineau, Alla Reznik, Lakehead Univ. (Canada)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The dark current (DC) in the X-ray photoconductors used in direct conversion X-ray detectors should be less than 1 – 10 pA/mm^2. To achieve this low level of DC within an amorphous lead oxide (a-PbO) detector, a thin layer of polyimide (PI) is used as a charge blocking layer. Here the DC vs. time behavior is characterized. A mathematical model is derived to simulate the observed DC kinetics and better comprehend the underlying DC mechanisms. This model gives insight into the electric field redistribution that occurs in PI/a-PbO detectors and charge collection efficiency.
12031-87
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Spectroscopic x-ray detectors (SXDs) count photons in two or more energy bins and enable single-shot material-specific and pseudo-monoenergetic imaging. Charge sharing is a non-ideality in these detectors which distorts incident spectra. One charge-sharing compensation method is to detect total-event counts (TC) and single-event counts (SC) simultaneously. The SC counters are incremented in an element only if there are no events in any neighboring element within a specified coincidence time window. We derive the covariance matrix for an SXD that records both TC and SC count data in multiple energy bins and verify the predicted covariances by simulation.
12031-95
Author(s): Maria Ruiz-Gonzalez, Robert G. Richards, Kimberly J. Doty, Phillip H. Kuo, Matthew A. Kupinski, Lars R. Furenlid, The Univ. of Arizona (United States); Michael A. King, Univ. of Massachusetts Medical School (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Ongoing developments in the field of molecular imaging have increased the need for gamma-ray detectors with better spatial resolution, while maintaining a large detection area. One approach to improve spatial resolution is to utilize smaller light sensors for finer sampling of scintillation light distribution. However, the number of required sensors per camera must increase significantly, which in turn increases the complexity of the imaging system. Here we present the design of a read-out electronics system that addresses these challenges. The read-out system, which is designed for a 10" x 10" SiPM-based scintillation gamma-ray camera, can process up to 162 light-sensor signals. This design can be adapted for other crystal/sensor configuration, and can be scaled for a different number of channels.
12031-108
Author(s): Corey Orlik, Stony Brook Medicine (United States); Sébastien Levéillé, Analogic Canada Corp. (Canada); Adrian F. Howansky, Jann Stavro, Scott Dow, Stony Brook Medicine (United States); Safa Kasap, Univ. of Saskatchewan (Canada); Amir H. Goldan, Wei Zhao, Stony Brook Medicine (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-117
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Amorphous Lead Oxide (a-PbO) photoconductor is considered as an alternative to amorphous Selenium X-ray–to–charge transducer for application in direct conversion digital X-ray detectors, which has a high potential for utilization in various fields of medical and industrial X-ray imaging. In this work, X-ray sensitivity W± of a-PbO is characterized in a wide range of electric fields, X-ray exposures and energies, and underlying mechanisms responsible for charge recombination are investigated. The peculiar dependencies of W± on these parameters lead to a conclusion that the columnar recombination mechanism dominates in a-PbO photoconductor with a secondary contribution of the bulk recombination.
12031-125
Author(s): Xu Ji, Mang Feng, Ran Zhang, Guang-Hong Chen, Ke Li, Univ. of Wisconsin-Madison (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The statistical distribution of the energy detected by a photon counting detector (PCD) from an input photon with given energy is referred as the energy response function. This work reports a physics-based model for energy response functions of CdTe-based PCDs. The model took all the possible physical processes in the diagnostic range into consideration (e.g., K-fluorescence photon generation and reabsorption, Compton scattering) and was capable of modeling the energy response function under the anti-coincidence mode. The model was validated using an experimental CdTe-based PCD using different input spectra.
12031-139
Author(s): Hamid Reza Yaghobi, Iran Univ. of Science and Technology (Iran, Islamic Republic of); Abdollah Pil-Ali, Univ. of Waterloo (Canada); Mohammad Azim Karami, Iran Univ. of Science and Technology (Iran, Islamic Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this work, a new low-power complementary metal-oxide-semiconductor (CMOS) image sensor with adjustable light sensitivity is designed. The main pixel comprises five separate transistors, and a comparator is used for the power consumption reduction with a pixel fill factor of 27%. Upon the pixel light-sensitive area receives light, the controller connects a supply which applies a 1.8 V to the main circuit, while in the absence of input light VDD is disconnected. This mechanism reduces the image sensor power consumption, where a power consumption of 2.9 µW (at 2.5 nA photodiode current) is achieved. The versatility of this approach allows it to be used in a wide range of medical applications, such as implantable biomedical devices. The proposed pixel is simulated using 0.18 - μm standard CMOS technology.
12031-141
Author(s): Kelsea P. Cronin, Matthew A. Kupinski, H. B. Barber, Lars R. Furenlid, The Univ. of Arizona (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Availability of photon-counting semiconductor detectors has recently increased as a result of improvements in crystal growth technology. These detector’s high energy resolutions and spatial resolutions make them ideal candidates for CT and SPECT applications. However most crystal materials still exhibit limitations imposed by hole transport and trapping. A further complication that is generally ignored is the generation of characteristic x-ray fluorescence photons that carry a fraction of the incident photon energy away from the primary interaction site. We have developed a monte-carlo charge transport and electrode-signal-induction code that simulates all of these effects in double-sided strip detectors. The simulation yields likelihoods that can be used with maximum likelihood techniques to estimate the interaction location, and whether the energy disposition is local, involves charge sharing, and/or involves fluorescence.
12031-149
Author(s): Hitalo R. Mendes, Alessandra Tomal, Univ. of Campinas (Brazil)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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This work aims to implement a detailed modeling for semiconductor detectors in the energy integrating (EI) and photon counting (PC) modes. The modeling was divided in two steps: radiation interaction and electron-hole pair (EHP) creation and dispersion. The results shows that the EI detector’s point response was wider when the incident energy increased and narrower with bias increase. Comparing the cases with and without dispersion, the relative difference between the central pixel and its adjacent equal to 96.1% and 88.8%, respectively. For PC mode, an energy threshold increases results in a narrower detector response and increase the image noise.
12031-159
Author(s): David Leibold, Stefan van der Sar, Marlies Goorden, Dennis Schaart, Technische Univ. Delft (Netherlands)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The point spread function (PSF) is commonly used to characterise detector performance. For energy-resolving photon-counting X-ray detectors, it is typically derived for each energy bin and determined under low count rate conditions, to avoid dead time and pile-up related distortions. While this renders the acquisition of a PSF straightforward, it does not fully characterise the spatial response of photon-counting detectors under all irradiation conditions encountered in clinical practice. To account for this, we propose a framework in which the detector PSF for each energy bin is determined for a given combination of incident spectrum and fluence rate.
Posters: Image Reconstruction
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-83
Author(s): Refik Mert Cam, Univ. of Illinois (United States); Umberto Villa, Washington Univ. in St. Louis (United States); Mark A. Anastasio, Univ. of Illinois (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The circular Radon transform (CRT) is widely employed as an imaging model for wave-based tomographic bioimaging modalities like ultrasound reflectivity tomography. A complete set of CRT data function is known to have redundancies. However, no explicit non-iterative image reconstruction method is known for inverting temporally-truncated data. To address this a learning-based approach is proposed to establish a filtered backprojection (FBP) method for use with half-time CRT data function. The learned half-time FBP achieves image quality comparable to a conventional FBP method that employs full-time data in the noiseless case and outperforms it in the noisy case.
12031-90
Author(s): Alma Eguizabal, Ozan Öktem, Mats U. Persson, KTH Royal Institute of Technology (Sweden)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Photon counting computed tomography (CT) provides additional energy information and improves material decomposition of CT images, but an important challenge in this new modality is how best to reconstruct multi-material images from the measured data. We present a deep learning-based solution based on a primal-dual-based one-step image reconstruction algorithm that maps photon count measurements directly to reconstructed basis images. We study a proof of concept on a set of 700 Shepp-Logan phantoms and demonstrate enhanced performance compared to a model-based two-step approach, as well as compared to considering deep learning only in the first step of the two-step solution.
12031-98
Author(s): Darin P. Clark, Cristian T. Badea, Duke Univ. Medical Ctr. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Novel deep learning (DL) methods have produced state-of-the-art results in nearly every area of x-ray CT data processing. However, DL-driven, iterative reconstruction remains challenging for volumetric reconstruction problems: the system matrix relating the projection and image domains is too large for network training. We formulate a cost function and supervised training approach which yield an analytical reconstruction sub-step, to transform between domains, and a regularization sub-step, which is consistent between iterations. Combined with projection and image domain splitting, these properties reduce the number of free parameters which must be learned, making volumetric, dual-domain data processing more practical.
12031-111
Author(s): Julian Bertini, Emil Sidky, Timothy J. Carroll, The Univ. of Chicago (United States); Yufen Chen, Northwestern Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Rosette trajectories have been used to measure time-dependent parameters of the MR signal, such as susceptibility differences, with high sensitivity for various applications. This non-Cartesian trajectory's distinctive feature is its high sampling rate of the central region of k-space in a short amount of time. This work focuses on applying an algebraic reconstruction technique (ART) and total variation (TV) constrained Chambolle-Pock primal-dual (CPPD) instance to reconstruct undersampled MRI data using a rosette trajectory, and we evaluate its ability to correct for undersampling artifacts in the face of coil sensitivity differences and phase roll.
12031-115
Author(s): George Yiasemis, The Netherlands Cancer Institute (Netherlands), Univ. of Amsterdam (Netherlands); Chaoping Zhang, The Netherlands Cancer Institute (Netherlands); Clara I. Sánchez, Univ. of Amsterdam (Netherlands); Jan-Jakob Sonke, The Netherlands Cancer Institute (Netherlands); Jonas Teuwen, The Netherlands Cancer Institute (Netherlands), Radboud Univ. Medical Ctr. (Netherlands), Univ. of Amsterdam (Netherlands)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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MRI is still a slow imaging modality. In clinical settings, subsampling the k-space measurements during scanning using Cartesian-rectilinear sampling, is currently the most conventional approach applied which is prone to producing aliased reconstructions. Involving Deep Learning results in reconstructing faithful images from subsampled data. Retrospectively applying a subsampling mask onto the k-space is a way of simulating the accelerated acquisition of k-space. In this paper we provide a review for the effect of applying either rectilinear or radial retrospective subsampling on the quality of the reconstructions outputted by trained Recurrent Inference Machines. Our results indicate that the model trained on radially subsampled data attains higher performance paving the way for other DL approaches to involve radial subsampling.
12031-129
Author(s): Dan Xia, Zheng Zhang, Buxin Chen, Emil Y. Sidky, Xiaochuan Pan, The Univ. of Chicago (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In cone-beam computed tomography (CBCT) imaging with an offset detector, due to the truncation of data at some views, data are required to be collected over a full angular range of 360 degree for successful analytical reconstruction. However, there exist interests of practical applications for limited-angular-range (LAR) imaging because it may allow for the reduction of radiation dose and scanning time and for the avoidance of the collisions between the moving gantry and scanned objects. In this work, we develop and investigate a directional-total-variation (DTV) algorithm for image reconstruction from partially truncated data collected over LARs. The results of the numerical studies demonstrate that the proposed algorithm can yield, from partially truncated LAR data, images with significantly reduced artifacts that are observed otherwise in images obtained with existing algorithms.
12031-138
Author(s): Zheng Zhang, Buxin Chen, Dan Xia, Emil Sidky, Xiaochuan Pan, The Univ. of Chicago Medicine (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In computed tomography (CT) imaging, recent developments in reconstruction algorithm and scan configuration design have provided useful tools for image reconstruction from data collected over a limited-angular range (LAR). In this work, we aim to investigate the impact of angular sampling interval on the accuracy of reconstruction from LAR data. In specific, we employ a two-orthogonal-arc scan configuration, and collect data from a numerical chest phantom over an LAR with various angular intervals. We then investigate image reconstruction by using the directional-total-variation (DTV) algorithm and evaluate reconstructions qualitatively and quantitatively. Results show that increased angular sampling interval can degrade image quality. Results of the simulation study also indicate an appropriate interval for sufficient reconstruction accuracy under specific imaging conditions, which provides insights for upper-bound performance of reconstructions in practical use.
12031-142
Author(s): Alessandra Costantino, Antony Bird, Univ. of Southampton (United Kingdom)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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A back-projection technique is used to deconvolve 3D near-field coded aperture images of gamma-ray sources. In this study back projection is combined with an image processing algorithm called CLEAN, which suppresses the artifacts created by the deconvolution process and improves the signal to noise ratio. Simulated and experimental data collected with a CZT dual-head coded aperture system are presented to demonstrate the capabilities of the reconstruction method to be applied to gamma-ray medical imaging.
12031-153
Author(s): Tiziano Natali, The Netherlands Cancer Institute (Netherlands); Zhiwei Zhai, Amsterdam UMC (Netherlands); Oleksandra Ivashchenko, Leiden Univ. Medical Ctr. (Netherlands); Jasper Smit, Matteo Fusaglia, Theo Ruers, The Netherlands Cancer Institute (Netherlands)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Intraoperative ultrasonography is the standard imaging practice during liver resection surgery. Although it can generate real-time high-quality images, it can only provide two-dimensional information. Therefore, the only way to generate a 3D visualization of the scanned area, and that does not add any additional cost to the procedure, is to reconstruct a volume from untracked 2D US images. We propose a CNN, which bases its estimation on speckle motion, that can determine the spatial coordinates of the images in a stack and then use them to generate a 3D reconstruction. We then improve the method with the implementation of a transducer specific attention map. The aim of this map is to help the CNN to focus on speckle motion in areas of the US images where the transducer is more accurate. The methods are evaluated on both synthetic and clinical data. It resulted that the implementation of the transducer specific attention map improved the reconstruction ability of the base model.
12031-162
Author(s): Mojtaba Zarei, Ehsan Abadi, Ehsan Samei, Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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This paper aims to cast a classical inverse optimization problem into a DNN-based framework to enhance the quality of the resulting images for CT image reconstruction. The deployed architecture takes advantage of a forward operator and its conjugate, which guides the network to extract the underlying information from the training data. We employed the state-of-the-art virtual imaging trial framework to cope with limitations on acquiring the training data and accessing the ground truth, which offers paired projection and ground truth data for anthropomorphic human models. Preliminary results demonstrated that the proposed framework outperforms standard methods such as the filtered back-projection method.
12031-167
Author(s): Buxin Chen, Zheng Zhang, Dan Xia, Emil Y. Sidky, Xiaochuan Pan, The Univ. of Chicago Medicine (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
Posters: Multi-Modality and Image Processing
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-79
Author(s): Kimberly Doty, Matthew A. Kupinski, R. Garrett Richards, Maria Ruiz-Gonzalez, The Univ. of Arizona (United States); Michael A. King, Univ. of Massachusetts Medical School (United States); Phillip H. Kuo, Lars R. Furenlid, The Univ. of Arizona (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Future scintillation gamma-ray detectors for single-photon emission computed tomography (SPECT) may incorporate a fiber optic plate to decrease the spread of the scintillation light. This may improve 3D position estimation by reducing the loss of spatial resolution caused by depth of interaction uncertainty. We have written a custom Monte Carlo simulation code to determine the effects of incorporating a fiber optic plate on the intrinsic spatial resolution of the detector by comparing the performance of planar detectors. The results are compared using Fisher Information Matrices and calculating the Cramér-Rao Lower Bounds on 3D position estimate variances
12031-91
Author(s): Qi Dong, Dong Huang, Ziqi Li, Yang Liu, Hongbing Lu, PLA Air Force Military Medical Univ. (China)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The segmentation of bladder tumors is an important step in extracting radiomics features of tumor regions, which can reflect the grade and stage of tumors. However, the existing researches still have several challenges, such as the lack of emphasis on salient areas and insufficient training samples. For these reasons, a segmentation model combining Attention-UNet and discriminator is proposed. Specifically, Attention-UNet can highlight salient regions for tumor segmentation task, thereby suppressing the region in the background that are not related to tumors. Besides, the discriminator network is used to expand the dataset, and this network may further optimize the tumor segmentation model by judging the true or false segmentation results. Extensive experimental results show that the proposed model achieves superior performance than the state-of-the-art methods. The Dice of the tumor area on training sets and test sets are 0.90 and 0.87, respectively.
12031-99
Author(s): You Hao, Jayaram K. Udupa, Yubing Tong, Caiyun Wu, Univ. of Pennsylvania (United States); Joseph M. McDonough, Carina Lott, Jason B. Anari, Patrick J. Cahill, The Children's Hospital of Philadelphia (United States); Drew A. Torigian, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The analysis of diaphragmatic motion is clinically important. We therefore propose a novel method for quantitative regional diaphragmatic motion analysis via dynamic free-breathing MRI. After 4D image construction in 51 normal children, we manually delineated the diaphragm on sagittal images. We then calculated and compared 13 regional velocities for each hemi-diaphragm-surface. We observed that regional velocities of the right hemi-diaphragm were almost always statistically significantly greater than those of the left hemi-diaphragm. Using this methodology, future larger scale prospective studies may be considered to confirm our findings and to quantitatively assess regional diaphragmatic dysfunction when various disease conditions are present.
12031-109
Author(s): Milad Diba, Shiva Abbaszadeh, Univ. of California, Santa Cruz (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-119
Author(s): Junha Park, Ecole Polytechnique, IPParis (France); Jean Rehbinder, iCube, Strasbourg Univ. (France); ‪Jérémy Vizet‬, Ecole Polytechnique, IPParis (France); André Nazac, Brugmann Univ. Hospital (Belgium); Angelo Pierangelo, Ecole Polytechnique, IPParis (France)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Cervical biomechanical properties change during pregnancy due to remodeling of the extracellular matrix (ECM). Many imaging techniques (X-ray, MRI, OCT, SHG, and others) have been used to characterize the microstructural and physiological changes of the human cervix during pregnancy. However, the clinical application of these techniques during pregnancy remains limited. In this study, a Mueller polarimetric colposcope (MPC) is used to probe the microstructure of the cervix in vivo during pregnancy. The MPC can simultaneously acquire polarimetric images at 550 and 650 nm with a large field of view (~5x5cm²) in about 1 second. The evolution of the main polarimetric parameters for the two wavelengths considered during pregnancy is investigated for a group of women with normal gestation.
12031-122
Author(s): Zheng Cao, Li Liu, BirenTech Research (China)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In image reconstruction tasks the measured data is usually limited due to physical constraints. Consequently it becomes an underdetermined inverse problem. Recently, using un-trained Deep Neural Network (DNN) by leveraging the DNN structure as a prior has been proved as a plausible approach. In this work, we propose a new un-trained image reconstruction neural network called EffiDecoder. We introduce mobile inverted bottleneck (MBconv) at each layer with computational-friendly depthwise separable convolutions. Also the feature channels are adaptively reduced. In accelerated MRI reconstruction tasks, we confirm great improvements in reconstruction efficiency. The image quality is also enhanced with fine details.
12031-133
Author(s): Patrick Marty, Christian Boehm, ETH Zurich (Switzerland); Catherine Paverd, Marga Rominger, UniversitätsSpital Zürich (Switzerland); Andreas Fichtner, ETH Zurich (Switzerland)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Full-waveform modelling serves as the basis for many emerging inversion techniques within ultrasound computed tomography. Being able to accurately depict strong material interfaces, such between soft tissue and bone, is particularly important for ensuring that these numerical methods produce physically correct results. We present a procedure for constructing digital twins of various parts of the human body through the use of conforming hexahedral meshes, which are used together with the spectral-element method to accurately model the interactions of the ultrasound wavefield at these sharp material boundaries. In silico cranial and knee phantoms are used as examples.
12031-144
Author(s): Luca Caucci, Harrison H. Barrett, The Univ. of Arizona (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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This paper presents new imaging systems for the estimation of biochemical interactions taking place inside a living organism. Understanding these interactions is critical in the development of effective therapeutic approaches. Our imaging systems use fast detectors for the estimation of various parameters of gamma rays or charged particles. These parameters include position, direction of propagation and energy. Recent work has shown that if all of these parameters are taken into account during reconstruction, the null space of the imaging system is strongly reduced or eliminated. This reduction in null space is critical to adequately characterize complicated physiological processes.
Posters: Phase Contrast
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-84
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this work, we propose an alternative approach to x-ray lithography which is based on conventional UV lithography that can potentially result in fabrication of high-aspect ratio structures, particularly x-ray absorption gratings. We have broken down a high-aspect ratio x-ray grating design into multiple layers of lower-aspect ratio structures and employed only the conventional UV~lithography. SU-8 photoresists are known for their multi-layer coating specification, which is used in this study. The new fabrication process proposed in this work results in a final high-aspect ratio x-ray absorption grating through accessible UV~lithography with lower cost and scaling-up compatibility, thus both research groups and industry can benefit from it. To the best of our knowledge, this is the first time a multi-layer x-ray grating design is proposed and reported. The output of this work can be used to perform large field-of-view high-energy coded-aperture x-ray phase-contrast imaging.
12031-86
Author(s): Sebastian Meyer, Serena Z. Shi, Nadav Shapira, Andrew D. A. Maidment, Peter B. Noël, Perelman School of Medicine, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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X-ray dark-field imaging measures small-angle scattering caused by the microstructure of granular materials and allows for quantitative evaluations that extend beyond conventional x-ray imaging. Wave-optics simulations were used to investigate the dark-field signal in speckle-based x-ray imaging. The dark-field signal showed a distinct dependence on sample structure and setup geometry but was affected by beam hardening. An accurate prediction of the signal strength was possible by using the mean frequency of the speckle power spectral density as the characteristic speckle size. Hence, x-ray speckles act as wave-front marker in the near-field regime and allow the inference of quantitative dark-field information.
12031-97
Author(s): Uttam Pyakurel, Laila Hassan, Carolyn MacDonald, Jonathan Petruccelli, Univ. at Albany (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Mesh-based phase imaging using conventional sources is simple and effective for imaging soft tissues. The use of polycapillary optics enhances the spatial coherence of the beam to improve the phase signature and image quality. Because the phase signature for the mesh-based technique is inherently smaller than for grating-based techniques, it is important to optimize the signal-to-noise ratio. Experimental and simulation analysis have been performed of SNR of phase and dark field images with and without polycapillary optics for a range of geometrical parameters and tube voltages and phantoms, including solid and porous objects embedded in muscle and fat and fresh and preserved mice.
12031-110
Author(s): Abdollah Pil-Ali, Sahar Adnani, Pranav Gavirneni, Seokjee Shin, Bahareh Sadeghimakki, Mahla Poudineh, William Wong, Karim Sallaudin Karim, Univ. of Waterloo (Canada)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Here we present a design and a fabrication process to fabricate high-aspect ratio gratings that benefit from a self-aligned hard-mask -- a patterned chromium-gold-chromium thin film deposited on a transparent ITO-on-glass substrate -- which facilitates both lithography and electroplating processes. The repeatability of the proposed method makes it suitable for achieving high-aspect ratio fine structures as thick as desired through a multi-layer structure without any restraint or limitation on the aspect ratio of features. The key advantage of this design is that it enables UV~lithography for high-aspect ratio grating fabrication through a reliable yet simple process. The proposed design and fabrication process help researchers further develop x-ray gratings performance to facilitate high-resolution coded-aperture and Talbot-Lau high-energy x-ray phase-contrast imaging. To the best of our knowledge, self-aligned multi-layer SU-8 based grating design has not been previously reported.
12031-121
Author(s): Jingcheng Yuan, Ian Harmon, Mini Das, Univ. of Houston (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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X-ray phase contrast imaging (PCI) has a great potential for improving the visibility of soft tissues in medical imaging. Single-mask edge-illumination (EI) method has been developed with the ability to obtain differential phase contrast with simpler experimental setup in comparison to grating based or conventional double mask EI PCI. We show results of single-mask PCI and results of differential phase contrast estimation in a single shot. While the system used a photon counting detector, spectral information was not used for this retrieval. The potential of this single mask PCI to reduce the radiation dose and improved contrast has not been fully investigated yet. In this work we compared the x-ray dose requirement of single-mask method with other methods by analyzing the SNR under different level of detector counts. We also propose and demonstrate a new model based on TIE for differential retrieval from single mask EI PCI with experimental data.
12031-130
Author(s): Abdollah Pil-Ali, Sahar Adnani, Univ. of Waterloo (Canada); Christopher Charles Scott, KA Imaging Inc. (Canada); Zain Hussain Warsi, Univ. of Waterloo (Canada); Alessandro Olivo, Univ. College London (United Kingdom); Karim Sallaudin Karim, Univ. of Waterloo (Canada)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Illumination curve is the key parameter that contributes to retrieving phase and absorption information in coded-aperture x-ray phase-contrast imaging. To obtain the illumination curve, multiple exposures are typically required which decreases x-ray dose efficiency and, more importantly, increases imaging time. Moreover, sample motion can negatively impact the image and information retrieval process. In this research, we employ a single mask in conjunction with a 7.8-$\mu$m pixel pitch amorphous selenium-CMOS hybrid direct conversion x-ray detector to obtain the beamlets' intensity profile with only a single exposure. We demonstrate how using an ultra-high spatial resolution x-ray detector with a single-mask CA technique can potentially increase both, dose efficiency and imaging time by at least a factor of 1.5X. Moreover, the resulting system using our approach is more compact with a source-to-detector distance of less than 30 cm.
12031-134
Author(s): Ivan Hidrovo, Joyoni Dey, Louisiana State Univ. (United States); Kyungmin Ham, Amitava Roy, Louisiana State Univ. (United States), Ctr. for Advanced Microstructures & Devices (United States); Les Butler, Louisiana State Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We have previously shown in simulations that X-ray Interferometry using Modulated Phase Gratings can create an interference pattern in clinical detectors from which attenuation, differential phase, and dark-field contrast images can be formed. These systems are useful because they eliminate the need to use the absorption grating in standard interferometry and thus can provide better X-ray dose efficiency. In this work we experimentally evaluate such a system using initial test gratings (from Microworks GmbH, Germany). After eliminating the effect of the source grating, we observed fringe patterns that are close to the theoretically expected period and visibility.
12031-146
Author(s): Abdollah Pil-Ali, Mohammad Soltani, Sahar Adnani, Muhammed Kayaharman, Mahla Poudineh, Bo Cui, Karim Salaudin Karim, Univ. of Waterloo (Canada)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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X-ray absorption gratings are the heart of coded-aperture and Talbot-Lau x-ray phase-contrast imaging (XPCi) techniques. The quality of imaging -- visibility -- in the aforementioned techniques is highly dependent on the quality of gratings. SU-8, an epoxy-based photoresist, is valued in Micro-Electro-Mechanical-Systems (MEMS) fabrication due to its excellent mechanical and optical properties. For fabricating x-ray absorption grating following the MEMS process, gold is the material most widely used to stop incident x-rays. In this work, we have investigated the adhesion quality of SU-8 to gold thin films for different adhesion promoter layers. We have employed a combination of a SU-8 thin film and a metallic-silane-based nanometer-thin film to improve the adhesion quality between SU-8 photoresist and a gold thin film substrate.
12031-152
Author(s): Laurène Quénot, Univ. Grenoble Alpes (France); Hélène Rougé-Labriet, Ecole de Technologie Supérieure (Canada); Sébastien Berujon, Univ. Federal do Rio de Janeiro (Brazil); Sylvain Bohic, Emmanuel Brun, INSERM - UA7 Strobe (France)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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X-ray phase contrast and dark-field imaging have a great potential for clinical diagnostic as one allows to visualize all kind of tissues within a single image with an improved contrast and the other permits to get information about unresolved microstructures such as lung alveoli. We propose here a simple experimental solution inspired by speckle-based imaging that solves many practical issues. It only involves a random attenuation mask on top of regular X-ray systems. The results constitute the proof of feasibility for PCI and dark-field imaging using low-cost materials available at standard X-ray laboratories.
12031-158
Author(s): Elizabeth Park, Louisiana State Univ. (United States); Jingzhu Xu, Univ. of Maryland School of Medicine (United States); Joyoni Dey, Louisiana State Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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A phase contrast system with a modulated phase grating (MPG) eliminates the need for an analyzer when compared to a standard Talbot Lau X-ray interferometer. This can provide three scans with the same total dose to the object as a standard mammogram. In this work, a hybrid MPG phase contrast system was investigated where the system fringe period can be varied, by changing a single system parameter, allowing interrogation at different resolutions and scatter lengths to further the variety of scans possible with the phase contrast system. The system can be also used for screening in a default setting.
12031-163
Author(s): Belinda Lim, Seyedamir Tavakoli Taba, The Univ. of Sydney (Australia); Benedicta D. Arhatari, Australian Synchrotron (Australia); Yakov Nesterets, Univ. of New England (Australia); Jane Fox, Beena Kumar, Monash Univ. (Australia); Daniel Hausermann, Anton Maksimenko, Christopher Hall, Australian Synchrotron (Australia); Matthew Dimmock, Monash Univ. (Australia); Darren Lockie, Maroondah BreastScreen (Australia); Mary Rickard, Nicola Giannotti, The Univ. of Sydney (Australia); Andrew Peele, Australian Synchrotron (Australia); Harry Quiney, The Univ. of Melbourne (Australia); Sarah Lewis, The Univ. of Sydney (Australia); Timur E. Gureyev, The Univ. of Melbourne (Australia); Patrick Brennan, The Univ. of Sydney (Australia)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Breast cancer persists as the greatest cause of cancer related mortality for women globally. Current breast imaging modalities have significant limitations requiring compromise between image quality, patient comfort and radiation dose. Phase-contrast imaging (PCI) therefore presents as a novel imaging solution for breast imaging with greater image quality achieved at a dose comparable to digital mammography without utilising compression. This study will attempt to optimise PCI to determine an optimal x-ray energy through objective physical measures of image quality. Objective data will also be analysed in conjunction with visual grading data to determine factors impacting visual grading of image quality.
12031-169
Author(s): Xin Zhang, Shenzhen Institutes of Advanced Technology (China), Chongqing Univ. (China); Ting Su, Jiecheng Yang, Jiongtao Zhu, Shenzhen Institutes of Advanced Technology (China); Dongmei Xia, Chongqing Univ. (China); Dong Liang, Yongshuai Ge, Shenzhen Institutes of Advanced Technology (China)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this abstract, we evaluate and compare the quantitative imaging performance of dual-energy CT (DECT) and differential phase contrast CT (DPCT) using the electron density and the effective atomic number. Both the contrast-to-noise-ratio (CNR) and modeled human observer results indicate that the DECT shows better quantitative imaging performance than DPCT at low spatial resolution (0.3mm), while the DPCT outperforms the DECT for ultra-high spatial resolution (0.03mm) imaging. At the 0.1mm spatial resolution, the DECT and DPCT shows similar quantitative imaging performance. This study provides a general guide for selecting the most appropriate quantitative X-ray CT imaging technique.
12031-172
Author(s): Jenny Romell, Exciscope AB (Sweden), KTH Royal Institute of Technology (Sweden); Jakob C. Larsson, Exciscope AB (Sweden); Hans M. Hertz, KTH Royal Institute of Technology (Sweden); William Twengström, Exciscope AB (Sweden)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We present an imaging system for x-ray phase-contrast computed tomography (CT) to be used complimentarily to classical histology. Our system uses a high-brightness x-ray source based on liquid-metal-jet technology, which enables cellular-resolution 3D imaging of centimeter-sized samples in a laboratory setting. A fresh or rapidly fixed tissue sample can be scanned to yield a cellular-resolution volume reconstruction, which afterwards can be sectioned in arbitrary directions to mimic histological slicing. Since the phase-contrast imaging is completely non-destructive, classical histology can always be performed afterwards, on regions-of-interest identified in the volume image.
Posters: Simulation and Phantoms
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-82
Author(s): Michael Geagan, Nadav Shapira, Kai Mei, Leening Liu, Univ. of Pennsylvania (United States); Grace J. Gang, J. Webster Stayman, Johns Hopkins Univ. (United States); Peter Noel, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We introduce IodinePrint, a dual-filament 3D printing solution for creating patient-based iodine-containing CT phantoms with accurate contrast, textures, and measured iodine concentration. A new iodine-doped 3D printing filament, PETG-I, was developed and combined with PLA filament to print geometric and anthropomorphic iodine-containing phantoms. Spectral CT images of these phantoms showed correctly rendered textures, accurate iodine concentration, and clear delineation of the enhanced features. Combining fine, localized control of iodine concentration with the proven attenuation modeling of PixelPrint demonstrates the feasibility of patient-based iodine-containing phantoms that will enable protocol optimizations and other applications in spectral CT research.
12031-94
Author(s): Azubuike Okorie, Delaware State Univ. (United States); Predrag Bakic, Univ. of Pennsylvania (United States); Sokratis Makrogiannis, Delaware State Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We introduce a methodology for simulation of mid-thigh anatomical structures, including individual muscle groups, trabecular and cortical bone, and regional adipose tissues. We calculated projections of the simulated mid-thigh phantom. We simulated thigh tissues of a healthy case, and a type-2 diabetes case by quadriceps shrinkage. We evaluated the effect of disease by calculating the areas of the healthy and the diseased tissues, and the Dice Similarity Coefficient between healthy and diseased tissues. We expect that our approach may be applied for data augmentation, designing clinical studies, and developing machine learning methods for characterization of metabolic and neuromuscular diseases.
12031-102
Author(s): Zhouyang Min, Thomas J. Sauer, William Paul Segars, Ehsan Abadi, Ehsan Samei, Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-112
Author(s): Sachin S. Shankar, Giavanna L. Jadick, Duke Univ. (United States); Eric A. Hoffman, Jarron Atha, Jessica C. Sieren, The Univ. of Iowa (United States); Ehsan Samei, Ehsan Abadi, Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Virtual Imaging Trials (VITs) provide many advantages over conventional trials. As a component of VIT platform being developed by CVIT, DukeSim is capable of virtually imaging high-resolution, voxelized phantoms, though validated against experimental measurements using cylindrical phantoms only. The purpose of this study was to validate DukeSim accuracy against a diseased-emulating anthropomorphic chest phantom ascertaining its realism in lung imaging research. Overall, DukeSim was found to be relatively accurate in lung imaging biomarker calculations using a relative error, demonstrating the validity of the program for VITs focused on lung imaging applications.
12031-113
Author(s): Ailin Wu, Xiao Jiang, Hehe Cui, Univ. of Science and Technology of China (China); Aidong Wu, Univ. of Science and Technology of China (China), The First Affiliated Hospital of USTC (China); Lei Zhu, Univ. of Science and Technology of China (China)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-127
Author(s): Joao P. V. Teixeira, Telmo M. Silva Filho, Thais G. do Rego, Yuri B. Malheiros, Univ. Federal da Paraíba (Brazil); Magnus Dustler, Predrag R. Bakic, Lund Univ. (Sweden); Trevor L. Vent, Raymond J. Acciavatti, Srilalan Krishnamoorthy, Suleman Surti, Andrew D. A. Maidment, Bruno Barufaldi, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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This work proposes the use of a novel Perlin-based phantom to optimize acquisition geometries in digital breast tomosynthesis (DBT). A novel open-source library (Perlin CuPy) was developed to accelerate the creation of Perlin phantoms. These phantoms were simulated using 3D models under mammography cranio-caudal compression. Breast and skin thickness, chest-wall to nipple distance, and Perlin parameters were varied. DBT projections and reconstructions were simulated using custom acquisition geometries of a next generation tomosynthesis system. These acquisition geometries were compared using breast volume estimates. Results show that volume estimates are improved with posteroanterior source motion, especially for thicker and larger breasts.
12031-136
Author(s): Matthew Wysocki, Scott Doyle, Univ. at Buffalo (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Prior research has shown that differences in data acquisition and data processing protocols significantly alter the quantitative morphological data obtained from 3D meshes. However, the impact of decimation (mesh complexity reduction) on quantitative morphological data is not well-understood. Six mesh complexity experimental groups of 3D anatomical models were generated from computed tomography (CT) data provided by the Jacobs School of Medicine and Biomedical Sciences, University at Buffalo (UB). Quantitative morphological data show statistically significant differences between experimental groups made up of different mesh complexities suggesting that consistent mesh complexities should be used to obtain more accurate morphological data.
12031-148
Author(s): Tyler Wilson, Siemens Healthineers (United States), Univ. of North Georgia (United States); Wesley Gohn, Francesc Massanes, Siemens Healthineers (United States); Maximilian Reymann, Siemens Healthineers GmbH (Germany); Hans Vija, Siemens Healthineers (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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A digital brain phantom was created from 3D CAD images and imported to a simulation of a Siemens Healthineers Symbia Evo SPECT system. The simulation was performed using GATE, a geant-4 based Monte Carlo toolkit, which included features to use 3D CAD structures. The brain volume was filled with water and configured emit radiation with energy spectrum matching that of Tc-99m with a radiation spatial distribution matching the geometry of the brain. The resulting simulation data was reconstructed using proprietary software. This will be a valuable tool for simulation that could be extended to other organs of the human anatomy.
Posters: X-ray, Fluoro, and Tomosynthesis
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-78
Author(s): Kyle A. Williams, Allison Shields, S. V. Setlur Nagesh, Daniel R. Bednarek, Stephen Rudin, Ciprian N. Ionita, Canon Stroke and Vascular Research Ctr. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Contrast dilution gradient (CDG) analysis is a technique used to extract velocimetric 2D information from digitally subtracted angiographic acquisitions. This information may then be used by clinicians to quantitatively assess the effects of endovascular treatment on flow conditions surrounding pathologies of interest. This study sought to resolve velocity distributions across vessel using 2D-CDG velocimetry using 1000 fps high speed angiography acquisitions. 2D-CDG analyses were compared with computational fluid dynamics via co-registration of the results from each velocimetry method. The study concluded CDG may be used to obtain velocity distributions in vascular pathologies given a continuous gradient of contrast.
12031-88
Author(s): Christina R. Inscoe, Alex J. Billingsley, Connor Puett, Otto Zhou, Jianping Lu, Yueh Lee, The Univ. of North Carolina at Chapel Hill (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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A patient-specific, low-dose scatter correction for digital chest tomosynthesis (DCT) has been demonstrated in human subjects for evaluation of lung lesion conspicuity. The method uses sparse primary beam samples to compute scatter and correct projection images prior to reconstruction. Thirty-five of fifty subjects have been imaged to date and will be followed with a reader study at the culmination of imaging to determine clinical efficacy. Preliminary radiologist impressions and sample images are included in this work.
12031-96
Author(s): Swetadri Vasan Setlur Nagesh, Allison Shields, Xinlin Wu, Ciprian N. Ionita, Daniel R. Bednarek, Stephen Rudin, Univ. at Buffalo (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this work, we present for the very first-time, orthogonal views of contrast injection in a 3D printed cerebral aneurysm model, acquired simultaneously using biplane 1000-fps x-ray High-speed Angiography imaging technique. The frontal plane images gave a better visualization of the flow streamlines in the parent artery in the inflow and outflow region of the aneurysm. The vortices within the aneurysm region especially near the dome were better visualized in the lateral plane images. Biplane HSAngio imaging techniques can give more accurate representations of 3-D blood flow within the complex vascular pathology of the human brain, compared to single-plane imaging.
12031-104
Author(s): Fateen Basharat, Jesse Tanguay, Ryerson Univ. (Canada)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We investigated the optimal exposure allocation for xenon-enhanced dual-energy (XeDE) radiography of lung function by simulation and experiment. Experiments were conducted using a custom-built x-ray imaging cabinet and a custom-built chest phantom representing an adult female chest and containing a simulated ventilation defect. We used contrast-to-noise ratio (CNR) normalized by the square root of total entrance exposure on the patient as a figure of merit. Our theoretical and experimental results show that the optimal exposure allocation is 0.5 indicating ∼2/3 of the total exposure should be allocated to low-energy image.
12031-114
Author(s): Diwash Thapa, Univ. of North Carolina at Chapel Hill School of Medicine (United States); Alex Billingsley, Yueting Luo, Christina Inscoe, Otto Zhou, Jianping Lu, The Univ. of North Carolina at Chapel Hill (United States); Yueh Lee, Univ. of North Carolina at Chapel Hill School of Medicine (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We have demonstrated the feasibility of whole-body skeletal imaging using orthogonal carbon nanotube-based tomosynthesis. We collimated the sources to a strip on a small field of view detector, employed a step-and-shoot data acquisition scheme, and reconstructed the imaging volume through piecewise tomosynthesis. Our system is comparable to conventional stationary digital tomosynthesis systems but can accommodate arbitrarily long imaging volumes. Our system also addresses the shortcomings of commercially available whole-body scanners for trauma imaging by obviating physical motion of the x-ray sources and minimizing the size of the system as desired for on-field applications such as trauma imaging.
12031-128
Author(s): Rie Tanaka, Kanazawa Univ. (Japan); William Paul Segars, Ehsan Abadi, Duke Univ. (United States); Shuhei Minami, Kanazawa Univ. Hospital (Japan); Ehsan Samei, Duke Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Ventilatory impairment is detected as decreased changes in lung density during respiration in dynamic chest radiography (DCR). The purpose of this study was to optimize imaging conditions in pediatric DCR through a virtual imaging trial (VIT). A breathing pediatric XCAT phantom with locally-collapsed lung was generated and was imaged at various combinations of imaging rate, tube voltage, and mAs/frame using an X-ray simulator. The maximum changes in pixel value (Δpixel values) during respiration were measured on an air sphere and its surrounding normal regions to calculate the contrast-to-noise ratio (CNR) of the Δpixel values. On the presupposition that the total entrance surface dose is less than twice as that in conventional pediatric chest radiography, we identified a higher mAs showed higher CNR, that is more advantageous for the detection of ventilator impairments. The VIT is useful to identify optimum imaging conditions and image quality in pediatric DCR.
12031-131
Author(s): Trevor L. Vent, Raymond J. Acciavatti, Chloe Choi, Bruno Barufaldi, Srilalan Krishnamoorthy, Penn Medicine (United States); Lucas Borges, Real-Time Tomography, LLC (United States); Johnny Kuo, Peter Ringer, Susan Ng, Real-Time Tomography, LLC (United States); Suleman Surti, Andrew D. A. Maidment, Penn Medicine (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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A next generation tomosynthesis (NGT) prototype has been developed to investigate alternative scanning geometries for digital breast tomosynthesis (DBT). Performance of NGT acquisition geometries is evaluated to validate previous phantom experiments. Two custom NGT acquisition geometries were compared to a conventional DBT geometry. Noise power spectra are used to describe features of specimen image reconstructions and compare acquisition geometries. NGT acquisition geometries improve high-frequency performance with superior isotropic super resolution, reduced out-of-plane blurring, and better overall reconstruction quality. NGT combines benefits of narrow- and wide-angle tomosynthesis in a single scan improving high-frequency spatial resolution and out-of-plane blurring, respectively.
12031-143
Author(s): Akyl Swaby, Shiva Abbaszadeh, Univ. of California, Santa Cruz (United States); Adam S. Wang, Martin J. Willemink, Stanford Univ. (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Dual energy imaging provides improved contrast for differentiating soft tissue from endogenous or exogenous materials such as bone or contrast agents. We propose a DL FPD with a direct conversion a-Se top layer and provide simulation results of the a-Se direct conversion layer to optimize the design parameters (e.g., the layer thickness and pixel pitch). Our intent is to maintain a spectral separation compared to that provided by a 200 µm CsI indirect conversion layer and improve image quality for future prototypes.
12031-151
Author(s): Akari Matsushima, Teikyo Univ. (Japan); Tai-Been Chen, I-Shou Univ. (Taiwan); Takahide Okamoto, Teikyo Univ. (Japan); Shih-Yen Hsu, I-Shou Univ. (Taiwan); Nana Itayama, Toru Ishibashi, Kazuyuki Fukuda, Teikyo Univ. (Japan)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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There are few reports on the detection of nodule in chest radiography using CNN. The accuracy of the reported detection was about 60-70%, and it is necessary to improve accuracy to ensure clinical reliability. In this study, we applied image processing combined contrast limited adaptive histogram equalization (CLAHE) with using wavelet de-noise processing for the nodule image in the chest radiography, using Faster R-CNN to try to improve the detection accuracy. As a result, when the number of anchors was set to 30 it was obtained detection rate of 81.8%.
12031-160
Author(s): Jonathan Troville, Rushikesh S. Dhonde, Stephen Rudin, Daniel R. Bednarek, Univ. at Buffalo (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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New photon-counting detectors (PCD) from Direct Conversion facilitate up to 1000 frames per second and 1 ms frame-acquisition times. When using these detectors for biplane contrast-media tracking in neuro-angiographic procedures, simultaneous acquisitions are utilized and cross-scatter between planes could degrade image quality. To quantify cross-scatter contributions, we model simultaneous biplane high-speed neuro-angiography in EGSnrc using the Zubal head phantom. Results indicate an increase in scatter due to cross-talk ranging from 4%-56% for AP projections and 48%-71% for lateral projections depending on detector orientation. This increase in scatter can be mitigated using anti-scatter grids, energy thresholding and increased air gaps.
12031-166
Author(s): Swetadri Vasan Setlur Nagesh, Allison Shields, Xinlin Wu, Ciprian Ionita, Daniel R. Bednarek, Stephen Rudin, Univ. at Buffalo (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Use of 1000fps HSAngio to compare the flow diversion effects of three stents with varying coverage density is presented here for the first time. Using 1000fps HSAngio technology interventionalist can visualize detailed blood flow patterns in real-time while performing the procedures. From these images, blood-velocity-distribution maps can derived. The velocity maps can be used to evaluate changes in blood flow within the aneurysm before and after placement of a treatment device such as a stent. Critical information such as an endoluminal leak which can cause treatment failure can also be detected.
12031-168
Author(s): Takahiro Mizoguchi, Tsutomu Gomi, Kitasato Univ. (Japan)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We have improved the image quality of low dose chest digital tomosynthesis (CDT) to achieve patient radiation dose reduction. But when low dose CDT is performed, its images is usually deteriorated. In particular, ground-glass-opacity (GGO) lesion have low contrast with the surrounding structures, which reduced visibility due to increased noise. Therefore, we adapted the novel image-to-image translation with a conditional generative adversarial networks to preserve the representation of GGO lesion. As a result, the noise reduction was achieved and structural similarity was improved. In conclusion, patient radiation dose reduction and usefulness of our method was anticipated.
12031-173
Author(s): Amar Prasad Gupta, Kyung Hee Univ. (Korea, Republic of); Jaekyu Jang, CAT Beam Tech Co., Ltd. (Korea, Republic of); Keunhwa Park, Kyung Hee Univ. (Korea, Republic of); Jaeik Jung, CAT Beam Tech Co., Ltd. (Korea, Republic of); YoungKuk Park, DRTECH Corp. (Korea, Republic of); Seung Jun Yeo, CAT Beam Tech Co., Ltd. (Korea, Republic of); Jeung Sun Ahn, Jehwang Ryu, Kyung Hee Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In fluoroscopy, X-rays are used to obtain the real-time moving images of the human anatomy. To do so, a pulsed fluoroscopy is used so that patient and staffs are exposed to minimum radiation possible. However, in medium priced pulsed fluoroscopy systems, X-ray tube without grid is used. The grid-less X-ray tube cannot produce perfect digital pulses due to inherent problem associated with thermionic emission of filament. This problem can be easily solved using carbon nanotube (CNT) based digital X-ray tubes. In this study, we have developed 120 kV CNT-based digital X-ray tubes for pulsed fluoroscopy that can be operated at very high frequency (~ MHz) producing low radiation dose during X-ray imaging.
12031-175
Author(s): Julien Rossignol, Philippe Marcoux, Gabriel Bélanger, Frédéric Gagnon, Patrick Dufour, Étienne Pilon, Louis-Daniel Gaulin, Univ. de Sherbrooke (Canada); Stefan Gundacker, RWTH Aachen Univ. (Germany); Audrey Corbeil Therrien, Jean-François Pratte, Yves Bérubé-Lauzière, Réjean Fontaine, Univ. de Sherbrooke (Canada)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Time-of-flight scatter rejection was proposed as a new alternative to minimize the adverse effects caused by scattered X-rays in radiography and computed tomography. A 16-channel time-correlated single photon counting detector with an expected overall timing jitter of 200 ps was designed to produce a first CT image using this technique to discriminate scattered photons from ballistic photons during an upcoming experiment at the Canadian Light Source. This contribution focuses on the design and characterization of this system.
12031-176
Author(s): Panyi Song, ALPhANOV/CELIA (France); Benjamin Barbrel, ALPhANOV (France); Fabien Dorchies, CELIA, Univ. de Bordeaux (France)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
12031-179
Author(s): Junyoung Park, Amar Prasad Gupta, Wooseob Kim, Jongmin Lim, Seungjun Yeo, Kyung Hee Univ. (Korea, Republic of); Dongkeun Kim, Astel Corp. (Korea, Republic of); Changwon Jeong, Kwon-Ha Yoon, Wonkwang Univ. Hospital (Korea, Republic of); Seungyong Cho, Jeung Sun Ahn, Mallory Mativenga, Jehwang Ryu, Kyung Hee Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Tomosynthesis is a technology that can diagnose chest diseases more effectively than a two-dimensional X-ray imaging system. To supplement the problems of the conventional tomosynthesis system, we developed a carbon nanotube-based field emission type stationary tomosynthesis system. Our chest tomosynthesis system was designed to obtain images from different angles by arranging small X-ray sources in an array. X-ray images were obtained using CNTs emitter based X-ray sources, and multi X-ray sources were pulsed through a field effect transistor (FET) circuit.
12031-180
Author(s): Jacob Collins, Jonathan Troville, Stephen Rudin, Daniel R. Bednarek, Univ. at Buffalo (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Machine learning (ML) models were investigated to automatically detect the patient head shift from isocenter and cephalometric landmark locations as a surrogate for head size. Fluoroscopic images of a Kyoto Kagaku anthropomorphic head phantom were taken at various head shifts and magnification modes, to create an image database. One ML model predicts the patient head shift and the other model predicts the coordinates of the anatomical landmarks. The goal is to implement these two separate models into the Dose Tracking System (DTS) developed by our group for eye-lens dose prediction and eliminate the need for manual input by clinical staff.
12031-183
Author(s): Raymond J. Acciavatti, Chloe J. Choi, Trevor L. Vent, Bruno Barufaldi, Andrew D. Maidment, Penn Medicine (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Our prototype next-generation tomosynthesis (NGT) system is a tool for investigating novel acquisition geometries in digital breast tomosynthesis (DBT). One such geometry is a non-isocentric acquisition in which the detector descends in the superior-to-inferior direction during the scan. The advantage of this geometry is examined through analysis of super-resolution (SR) with a high-frequency test pattern. In clinical DBT reconstructions, SR is only achieved if the test frequency is oriented parallel with the direction of source motion. In the non-isocentric geometry, SR can be achieved isotropically; that is, for all orientations of the test pattern.
12031-185
Author(s): Julien Rossignol, David Gaudreault, Gabriel Bélanger, Étienne Pilon, Audrey Corbeil Therrien, Yves Bérubé-Lauzière, Réjean Fontaine, Univ. de Sherbrooke (Canada)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Time-correlated single photon counting detectors, such as those used in positron emission tomography and high energy physics offer a new alternative to reduce the effects of scattered photons in computed tomography and X-ray radiography by discriminating scattered photons based on their time-of-flight from the source to the detector. A system-wide timing jitter of 10 ps or less is required to remove most scattered photons and prevent their adverse effects on image quality. This paper provides an overview of TOF scatter rejection and presents several challenges that must be overcome to achieve a clinical use of this technique.
12031-186
Author(s): Chloe J. Choi, Trevor L. Vent, Andrew D. A. Maidment, Univ. of Pennsylvania (United States)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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The impact of the angular range in conventional DBT is a trade-off in image quality; increasing angular range improves in-depth resolution and isotropic sampling across the detector, but compromises in-plane resolution. Our next generation tomosynthesis (NGT) system is capable of two-dimensional source trajectory and incorporates narrow- and wide-angle acquisition in orthogonal directions for a single tomosynthesis scan. In this work, performance of NGT geometries for high- and low-frequency objects across the detector was evaluated via computer simulations. We showed that NGT geometries preserve high in-plane resolution and present highly isotropic sampling, thus combine the benefits of narrow- and wide-angle tomosynthesis.
12031-189
Author(s): Jaekyu Jang, CAT Beam Tech Co., Ltd. (Korea, Republic of); Amar Prasad Gupta, Keunhwa Park, Kyung Hee Univ. (Korea, Republic of); Seung Jun Yeo, Jaeik Jung, CAT Beam Tech Co., Ltd. (Korea, Republic of); Jeung Sun Ahn, Jehwang Ryu, Kyung Hee Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this study, we report a development of 120 kV ceramic-based filament type X-ray tube for panoramic dental imaging. We have compared in-house built ceramic X-ray tube with commercial glass X-ray tube which is most commonly used for 100 kV panoramic dental X-ray imaging system. The result shows that despite the 38 % reduction in size, ceramic tube has better IV characteristic with similar filament size and higher limiting spatial resolution compared to glass X-ray tube. Moreover, we have successfully performed all the X-ray imaging experiments using 100 kV 500W custom built high voltage source.
12031-190
Author(s): Qinghua Liao, Xiaoyu Lai, Ctr. for Tuberculosis Control of Guangdong Province (China); Li Xia, Fleming Y. M. Lure, Shenzhen Zhiying Medical Imaging (China); Stefan Jaeger, U.S. National Library of Medicine (United States); Lingjun Qian, Qian Xiao, Lin Guo, Shenzhen Zhiying Medical Imaging (China)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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In this work, we developed an advanced registration model by segmentation mask to assist radiologists in the chest radiographs detection of interval changes. ResUnet network consisted of an encoder-decoder was trained as a registration structure. This is the first work to conduct chest radiographs registration by segmentation mask. We demonstrated the use of mask is an efficient way to improve registration accuracy.
12031-193
Author(s): Amar Prasad Gupta, Jinho Choi, Kyung Hee Univ. (Korea, Republic of); Jaekyu Jang, Seung Jun Yeo, Jaeik Jung, CAT Beam Tech Co., Ltd. (Korea, Republic of); Jeung Sun Ahn, Kyung Hee Univ. (Korea, Republic of); Beom-Seok Ko, Asan Medical Ctr. (Korea, Republic of); Jehwang Ryu, Kyung Hee Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Recently, field emitters such as Silicon, carbon nanotube (CNT) and diamond based X-ray sources have shown metamorphic phase transformation by leaping a valley of death from lab to market. This trend shows a huge potential in fully digitalizing more than 100-year-old X-ray technology. Our research group have recently published the paper explaining the feasibility of CNT emitter based X-ray source as a portable intraoperative specimen radiographic system. However, the system had poor spatial resolution quality and system required optimization of size. In this study, we have investigated the effect of changing the length of self-focusing gate structure on the spatial resolution capability of X-ray system.
12031-194
Author(s): Sunghoon Choi, Jin-Woo Jeong, Electronics and Telecommunications Research Institute (Korea, Republic of); Geun-Young Oh, Dong-Il Kim, Jin-Gyu Yang, Picopack Inc. (Korea, Republic of); Yoon-Ho Song, Electronics and Telecommunications Research Institute (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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Intraoral dental x-ray exams are the cost-effective way for early detection of decay between teeth. However, the transmitted x-ray photons, which are not absorbed in the FOP, directly hit the end of CMOS sensors, thus results in the salt and pepper noise on the image. Another issue is the optimization of contrast and brightness when the foreign bodies exist in tooth. A standard tone-mapping operator (TMO) utilizes a full scale of 14-bit gray range so that a whole pixel values are distributed over the limited range. A deep neural network-based approach was proposed to expand the LDR image to synthetic high dynamic range (HDR) image in order to enhance the contrast of the soft tissue after the TMO. We have also conducted the adaptive median filtering to reject the salt and pepper noise on the images before the network training. The results indicated that the noise-corrupted LDR images were optimally reconstructed into HDR images for both simulation and experiment dataset.
12031-197
Author(s): Jongmin Lim, Amar Prasad Gupta, Kyung Hee Univ. (Korea, Republic of); Jaeik Jung, Jaekyu Jang, CAT Beam Tech Co., Ltd. (Korea, Republic of); Jinho Choi, Kyung Hee Univ. (Korea, Republic of); Seung Jun Yeo, Kyung Hee Univ. (Korea, Republic of), CAT Beam Tech Co., Ltd. (Korea, Republic of); Seung-bum Ryu, Dexcowin Co., Ltd. (Korea, Republic of); Jeung Sun Ahn, Jehwang Ryu, Kyung Hee Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We designed the handheld type x-ray application using a carbon nanotube cold cathode electron gun. By fabricating the carbon nanotube(CNT)-based x-ray tube which is the core of the system, and showed that the tube has the performance necessary to acquire images. We also succeeded in obtaining X-ray images of human teeth using their own tube and X-ray generation system.
12031-199
Author(s): Hanna Lee, Jinho Choi, Amar Prasad Gupta, Kyung Hee Univ. (Korea, Republic of); Jaeik Jung, Jaekyu Jang, CAT Beam Tech Co., Ltd. (Korea, Republic of); Kyung-Sik Yoon, Jehwang Ryu, Kyung Hee Univ. (Korea, Republic of)
In person: 21 February 2022 • 5:30 PM - 7:00 PM
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We designed a carbon nanotube (CNT)-based microbeam system capable of irradiating cells. The functionality of the designed system was verified while checking the physical properties, and the suitability of the system as a cell irradiator was confirmed while checking the biological properties.
Session 8: Spectral CT
In person: 22 February 2022 • 8:00 AM - 9:40 AM
Session Chairs: Patrick J. La Rivière, The Univ. of Chicago (United States), Mini Das, Univ. of Houston (United States)
12031-38
Author(s): Maria Jose Medrano Matamoros, Xinyuan Chen, Tao Ge, Washington Univ. in St. Louis (United States); Tianyu Zhao, Washington Univ. in St Louis (United States); Rui Liao, David G. Politte, Jeffrey F. Willamson, Washington Univ. in St. Louis (United States); Bruce R. Whiting, Yao Hao, Baozhou Sun, Washington Univ. in St Louis (United States); Joseph A. O'Sullivan, Washington Univ. in St. Louis (United States)
In person: 22 February 2022 • 8:00 AM - 8:20 AM
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Accuracy in proton range prediction is critical in proton therapy to ensure conformal tumor dose. Our lab proposed a joint statistical image reconstruction method (JSIR) based on a basis vector model (BVM) for estimation of stopping power ratio maps and demonstrated that it outperforms competing Dual Energy CT (DECT) methods. However, no study has been performed on the clinical utility of our method. Here, we study the resulting dose prediction error, the difference between the dose delivered to tissue based on the more accurate JSIR-BVM method and the planned dose based on Single Energy CT (SECT).
12031-39
Author(s): Yinsheng Li, Ke Li, John Garrett, Guang-Hong Chen, Univ. of Wisconsin School of Medicine and Public Health (United States)
In person: 22 February 2022 • 8:20 AM - 8:40 AM
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CT imaging is one of the primary diagnostic medical imaging modalities. However, there is a long-standing technical limitation associated with conventional CT imaging: anatomical structures with different material compositions may have the same CT number, thereby limiting the ability to differentiate and classify different tissue types and contrast agents. To address this limitation, the currently available strategy is to modify the hardware acquisition systems such that dual energy CT (DECT) data acquisition scheme can be accommodated. In this work, we show that the elemental composition of a material can be directly extracted from a conventional single-kV CT acquisition without invoking DECT acquisition scheme.
12031-40
Author(s): Patrick D. VanMeter, Kishore Rajendran, Lifeng Yu, Cynthia McCollough, Shuai Leng, Mayo Clinic (United States)
In person: 22 February 2022 • 8:40 AM - 9:00 AM
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Coronary CT Angiography (cCTA) is commonly used to detect and quantify luminal stenoses in patients with coronary artery disease (CAD). However, its use is limited in patients with heavy coronary calcifications due to calcium blooming, which is caused by insufficient spatial resolution. This study evaluated the ability of a photon-counting-detector (PCD) CT in quantifying of luminal stenosis in the presence of heavy calcifications relative to an energy-integrating-detector (EID) CT. The phantom results indicate that with PCD-CT, luminal stenoses that were previously considered non-assessable due to the presence of heavily-calcified plaques can be assessed using cCTA.
12031-41
Author(s): Ke Li, Yinsheng Li, Univ. of Wisconsin-Madison (United States); Zhihua Qi, Henry Ford Health System (United States); John W. Garrett, Thomas M. Grist, Guang-Hong Chen, Univ. of Wisconsin-Madison (United States)
In person: 22 February 2022 • 9:00 AM - 9:20 AM
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Towards providing a “one-stop-shop” solution to anatomical and parenchymal perfusion imaging for pulmonary embolism (PE) evaluation, this work developed a method that uses a deep neural network to estimate effective atomic number (Zeff) information embedded in single-kV pulmonary CT angiography projection data. Based on the estimated Zeff map and the definition of perfusion blood volume (PBV), quantitatively accurate PBV maps can be generated. A multi-center human subject study demonstrates that the proposed single-kV CT and Zeff based PBV method provides a more sensitive and specific biomarker to quantify pulmonary perfusion defects compared with the iodine material image-based perfusion estimation method.
12031-42
Author(s): Leening P. Liu, Michael C. Soulen, Peter B. Noël, Nadav Shapira, Univ. of Pennsylvania (United States)
In person: 22 February 2022 • 9:20 AM - 9:40 AM
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Thermal hepatic ablation procedures are currently performed without actively monitoring temperature distributions, leading to higher than desired rates of incomplete ablations, local recurrences, and damage to surrounding structures. Correlation of physical density and temperature changes associated with thermal volumetric expansion is optimal for isolating temperature associated effects. Our previously developed physical density result from spectral CT was validated with high agreement between weight measurements and calculated mass from physical density maps. Physical density during heating and cooling of ex vivo bovine muscle exhibited a strong relationship to temperature changes, thus establishing its utility for non-invasive real-time temperature monitoring in ablation.
Session 9: Cone-Beam CT
In person: 22 February 2022 • 10:10 AM - 12:10 PM
Session Chairs: Joseph W. Stayman, Johns Hopkins Univ. (United States), John I. Yorkston, Carestream Health, Inc. (United States)
12031-43
Author(s): Mang Feng, Xu Ji, Kevin Treb, Ran Zhang, Sarvesh Periyasamy, Paul Laeseke, Ke Li, Univ. of Wisconsin School of Medicine and Public Health (United States)
In person: 22 February 2022 • 10:10 AM - 10:30 AM
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In this work, a unified framework was developed to jointly address scatter artifacts, detector nonuniformity-induced concentric artifacts, and beam hardening artifacts in C-arm photon counting detector (PCD) cone beam CT. By leveraging the energy-resolving capability of PCDs, a better estimation of the scattered photon signal was obtained via a photoelectric-Compton scattering decomposition. Next, detector nonuniformity and beam hardening artifacts were jointly corrected via a second-round projection domain pixel-wise material decomposition. Both phantom and in vivo animal results demonstrated that the proposed correction method generated high-quality and quantitative PCD cone beam CT images for image-guided interventions.
12031-44
Author(s): Alejandro Sisniega, Alexander Lu, Heyuan Huang, Wojciech Zbijewski, Mathias Unberath, Jeffrey H. Siewerdsen, Clifford R. Weiss, Johns Hopkins Univ. (United States)
In person: 22 February 2022 • 10:30 AM - 10:50 AM
12031-45
Author(s): Tom Russ, Medical Faculty Mannheim, Heidelberg Univ. (Germany); Yiqun Q. Ma, Johns Hopkins Univ. (United States); Alena-Kathrin Golla, Dominik F. Bauer, Medical Faculty Mannheim, Heidelberg Univ. (Germany); Tess Reynolds, The Univ. of Sydney (Australia); Christian Tönnes, Medical Faculty Mannheim, Heidelberg Univ. (Germany); Sepideh Hatamikia, Austrian Ctr for Medical Innovation and Technology (ACMIT) (Austria), Ctr for Medical Physics and Biomedical Engineering (Austria); Lothar R. Schad, Frank G. Zöllner, Medical Faculty Mannheim, Heidelberg Univ. (Germany); Grace J. Gang, Wenying Wang, J. Webster Stayman, Johns Hopkins Univ. (United States)
In person: 22 February 2022 • 10:50 AM - 11:10 AM
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The aim of this work is to extend a previously proposed framework for fast reconstruction of non-circular CBCT trajectories. The pipeline combines a deconvolution operation on the backprojected measurements using an approximate, shift-invariant system response prior to processing with a CNN. We trained and evaluated the CNN for this approach using 1800 randomized arbitrary orbits formed from 1000 procedurally generated tetrahedral phantoms as well as anthropomorphic data in the form of 800 CT and CBCT. Using this proposed reconstruction pipeline, computation time was reduced by 90\% as compared to MBIR with only minor differences in performance. Our results suggest the potential for fast processing of arbitrary CBCT trajectory data with reconstruction times that are clinically relevant and applicable - facilitating the application of non-circular orbits in CT image-guided interventions and intraoperative imaging.
12031-46
Author(s): Stephen Z. Liu, Chumin Zhao, Johns Hopkins Univ. School of Medicine (United States); Magdalena Herbst, Thomas Weber, Sebastian Vogt, Ludwig Ritschl, Steffen Kappler, Siemens Healthineers (Germany); Jeffrey H. Siewerdsen, Wojciech Zbijewski, Johns Hopkins Univ. School of Medicine (United States)
In person: 22 February 2022 • 11:10 AM - 11:30 AM
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We investigated the feasibility of detection and quantification of bone marrow edema (BME) using dual-energy (DE) Cone-Beam CT (CBCT) with a dual-layer flat panel detector and three-material decomposition. A realistic CBCT system simulator was applied to study the impact of detector quantization, scatter, and spectral calibration errors on the accuracy of fat-water-bone decompositions of dual-layer projections. The digital phantom consisted of a water cylinder with nine inserts, each containing a unique mixtures of water-fat-cortical bone; decreasing fractions of fat indicated increasing degrees of BME. A two-stage three-material (water-bone-fat) DE decomposition was applied to the data. Various detector quantization levels, scatter (including scatter correction error and beam collimation) and spectral calibration error (over-/underestimation of source filtration) were simulated, and there effects on the three-material decompositions were analyzed.
12031-47
Author(s): Chengzhu Zhang, Yinsheng Li, Guang-hong Chen, Univ. of Wisconsin-Madison (United States)
In person: 22 February 2022 • 11:30 AM - 11:50 AM
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Previous work demonstrated the success of a new pathway to enable shift-invariant and scalable ROI reconstruction for short-scan and super-short-scan fan-beam CT geometry. Two advances are presented in this work: 1) Its generalization to 3D volume-of-interest (VOI) reconstruction for short-scan circular cone-beam geometry; 2) The feasibility of undersampled cone-beam VOI reconstruction. Specifically, the reconstruction framework trained by 2D numerical data is directly applied to 3D human subject reconstruction in a slice-by-slice manner. The experimental studies demonstrated the feasibility of 82-view short-scan cone-beam reconstruction for a VOI of 5 cm by 5 cm by 3.2 cm using a 64-slice CT scanner.
12031-48
Author(s): Farhang Bayat, Mohamed E. Eldib, Cem Altunbas, Univ. of Colorado Anschutz Medical Campus (United States)
In person: 22 February 2022 • 11:50 AM - 12:10 PM
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Simultaneous use of kilovoltage (kV) and megavoltage (MV) beams has numerous potential applications in CBCT-guided radiotherapy. However, simultaneous kV and MV beam delivery contaminates kV projections with MV cross-scatter, deteriorating the CBCT image quality. This work investigates the utility of 2D antiscatter grids and a novel scatter correction method in reducing the effects of MV cross-scatter. Through multiple experiments, this work, for the first time, demonstrates that high energy scattered radiation can be effectively suppressed by using combined hardware and software-based scatter mitigation methods, and may enable high fidelity kV CBCT imaging in the presence of MV beam irradiation.
Session 10: Simulation and Phantoms
In person: 22 February 2022 • 1:20 PM - 3:00 PM
Session Chairs: Joseph Y. Lo, Carl E. Ravin Advanced Imaging Labs. (United States), Adam M. Alessio, Michigan State Univ. (United States)
12031-49
Author(s): Lavsen Dahal, JP Foundation (Nepal); Fakrul Islam Tushar, Ehsan Abadi, Maciej Mazurowski, W. Paul Segars, Ehsan Samei, Joseph Y. Lo, Duke Univ. (United States)
In person: 22 February 2022 • 1:20 PM - 1:40 PM
12031-50
In person: 22 February 2022 • 1:40 PM - 2:00 PM
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Since the introduction of MC-GPU in 2009, fast Monte Carlo-based x-ray transport simulation codes executed in GPUs have been successfully used to study sophisticated x-ray imaging applications that would require excessive computational resources using general-purpose codes, such as large-scale virtual clinical trials. We present an updated version of the code, MC-GPU v2.0, that includes multiple algorithmic improvements that maximize the performance in state-of-the-art GPUs. New features that extend the applicability of the software are described. The implementation of analytical quality control phantoms that allow for a direct comparison of a clinical mammography device with its virtual twin is also presented.
12031-51
Author(s): Astrid Van Camp, KU Leuven (Belgium); Hilde Bosmans, UZ Leuven (Belgium), KU Leuven (Belgium); Philippe Lambin, Maastricht Univ. (Netherlands); Nicholas Marshall, UZ Leuven (Belgium), KU Leuven (Belgium); Lesley Cockmartin, UZ Leuven (Belgium); Henry Woodruff, Manon Beuque, Maastricht Univ. (Netherlands)
In person: 22 February 2022 • 2:00 PM - 2:20 PM
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Given the importance of detecting and characterizing microcalcification clusters, we present a method to generate synthetic cases of (contrast-enhanced) mammography with calcification clusters for future training of CAD algorithms or related processing. The method consists of combining individual microcalcifications into benign and malignant clusters. Benign calcifications were mathematically created, while the malignant calcifications were segmented from real clusters. With an automated approach to distribute the calcifications in clusters and to select regions of interest in the mammograms, synthetic hybrid mammograms can be simulated. Pending final approval of their realism, large amounts of synthetic data can be made available.
12031-52
Author(s): Noelia Solís Preciado, Christian Fedon, Radboud Univ. Medical Ctr. (Netherlands); Melissa L. Hill, Volpara Health Technologies Ltd. (New Zealand); Ioannis Sechopoulos, Radboud Univ. Medical Ctr. (Netherlands), Dutch Expert Centre for Screening (LRCB) (Netherlands)
In person: 22 February 2022 • 2:20 PM - 2:40 PM
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The aim of this study is to evaluate the accuracy of one of the commercial breast density quantization software. The estimation is performed by means of mammograms of five different 3D printed breast phantoms obtained from clinical patient images. Mammograms of these phantoms were acquired and analyzed using the density software. In addition, the spectra used by the automatic exposure control for each mammogram was accurately characterized and modeled using a previously published spectral model. The result is that the amount of estimated dense tissue is accurate to within an intra-phantom mean of 3.5% (std. dev. <2.8%), with negligible bias.
12031-53
Author(s): Aunnasha Sengupta, U.S. Food and Drug Administration (United States), Univ. of Michigan (United States); Andreu Badal, Aldo Badano, U.S. Food and Drug Administration (United States)
In person: 22 February 2022 • 2:40 PM - 3:00 PM
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We report on in silico versions of the FujiFilm Innovality and Hologic Selenia Dimensions DBT systems, based on a previously developed direct detector model. We have also modeled the performance of the cesium iodide(CsI:Tl) based indirect detector, currently used in the GE SenoClaire system. The CsI model includes the depth-dependent spread and collection of the optical photons, implemented using a pre-computed set of point response functions that describe the optical spread as a function of depth. Standard image quality metrics, such as modulation transfer function (MTF), noise power spectrum (NPS), and detective quantum efficiency (DQE)were used to validate the models.
Session 11: Image Reconstruction
In person: 22 February 2022 • 3:30 PM - 4:50 PM
Session Chairs: Frédéric Noo, The Univ. of Utah (United States), Seungryong Cho, KAIST (Korea, Republic of)
12031-54
Author(s): Luke Lozenski, Washington Univ. in St. Louis (United States); Mark Anastasio, Univ. of Illinois (United States); Umberto Villa, Washington Univ. in St. Louis (United States)
In person: 22 February 2022 • 3:30 PM - 3:50 PM
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Dynamic imaging plays a fundamental role in studying various biological phenomena but faces two challenges: data incompleteness and computational burden. This contribution investigates the use of implicit neural representations (INRs) to address both. Using INRs, a dynamic image reconstruction method is proposed, where a continuous mapping from spatiotemporal coordinates to a scalar value representing the object is learned directly from measurement data. As such, it is fundamentally different from other learning methods requiring large datasets of training images. The feasibility of the proposed framework is illustrated with an application to dynamic image reconstruction from undersampled circular Radon transform data.
12031-55
Author(s): Aina Tersol, Pengwei Wu, Johns Hopkins Univ. (United States); Rolf Clackdoyle, Univ. Grenoble Alpes (France); John M. Boone, Univ. of California, Davis (United States); Jeffrey Siewerdsen, Johns Hopkins Univ. (United States)
In person: 22 February 2022 • 3:50 PM - 4:10 PM
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Cone-beam sampling effects are challenging to assess in a rigorous, quantitative manner, motivating us to present a generalizable figure of merit (based on the minimum ray angle through a point in the object) for cone-beam sampling completeness in relation to physical measurements of artifact magnitude in a common disk-pair phantom. Experiments were conducted using two CBCT systems (mobile C-arm and extremity scanner) capable of circular, tilted, non-circular, and/or multi-source orbits. Consistent correlation was found between minimum ray angle and artifact magnitude, indicating that minimum ray angle presents a decent surrogate for the completeness of cone-beam sampling.
12031-56
Author(s): Varun A. Kelkar, Mark A. Anastasio, Univ. of Illinois (United States)
In person: 22 February 2022 • 4:10 PM - 4:30 PM
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In order to regularize an ill-posed imaging inverse problem, prior knowledge about the object to-be-imaged must be utilized. Deep learning approaches are being actively investigated for this purpose. This work uses a style-based generative adversarial network (StyleGAN) to constrain an image reconstruction problem when a prior image of the object is available. An optimization problem is formulated in the disentangled latent-space of a StyleGAN, which is used to impose prior image-based constraints onto meaningful image attributes. The superiority of the proposed approach as compared to classical approaches is demonstrated using a stylized numerical study inspired by MR imaging.
12031-57
Author(s): Darin P. Clark, Ehsan Abadi, Nicholas Felice, W. Paul Segars, Ehsan Samei, Cristian T. Badea, Duke Univ. Medical Ctr. (United States)
In person: 22 February 2022 • 4:30 PM - 4:50 PM
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Cardiac CT imaging is invaluable in a variety of applications including non-invasive assessment of coronary artery calcifications, the ruling out of acute coronary artery syndrome, and the planning of valve replacement procedures. To facilitate advancements in research and clinical practice, we developed a vendor-neutral pipeline for virtual imaging trials involving cardiac CT simulation and reconstruction. Specifically, we developed a reconstruction method to be combined with dynamic virtual patients (XCAT) and a CT simulator (DukeSim) to generate realistic, retrospectively gated, helical cardiac CT projection data sets. Analytical and iterative reconstruction methods were investigated and analyzed with image quality and functional metrics.
Session 12: PCD-CT Evaluation and Applications
In person: 23 February 2022 • 8:00 AM - 9:40 AM
Session Chairs: Thomas Flohr, Siemens Healthineers (Germany), Jinyi Qi, Univ. of California, Davis (United States)
12031-58
Author(s): Zaki Ahmed, Kishore Rajendran, Hao Gong, Cynthia McCollough, Shuai Leng, Mayo Clinic (United States)
In person: 23 February 2022 • 8:00 AM - 8:20 AM
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Dual-source CT can provide high temporal resolution single energy imaging, but this is sacrificed in dual-energy mode (DS-DE). The current study evaluates a dual-source photon-counting-detector (DS-PCD) CT capable of high temporal resolution (66 ms) dual-energy imaging. A rod simulating coronary artery motion was scanned on both DS-DE and DS-PCD CT scanners and image quality was evaluated. Spatial registration—quantified by dice coefficient—between the high and low energy images was better with DS-PCD than DS-DE. Furthermore, the rod had a more circular appearance and its diameter was more accurate in iodine maps generated by DS-PCD whereas DS-DE suffered from motion artifacts.
12031-59
Author(s): Thomas W. Holmes, Emory Univ. (United States); Bernhard Schmidt, Thomas Flohr, Stefan Ulzheimer, Siemens Healthcare (Germany); David A. Bluemke, Univ. of Wisconsin-Madison (United States); Amir Pourmorteza, Emory Univ. (United States), Georgia Institute of Technology (United States)
In person: 23 February 2022 • 8:20 AM - 8:40 AM
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We compared CT x-ray beam-hardening artifacts in a hybrid scanner with energy-integrating detectors (EID) versus photon-counting detectors (PCD) subsystems. EID-CT images had less beam hardening artifacts compared to PCD-CT images for x-ray tube voltages 120 kVp and higher. We further demonstrated that the inherent spectral information of PCDs can be used to effectively eliminate beam-hardening artifacts.
12031-60
Author(s): Ehsan Abadi, Cindy McCabe, Saman Sotoudeh-Paima, William Paul Segars, Ehsan Samei, Duke Univ. (United States)
In person: 23 February 2022 • 8:40 AM - 9:00 AM
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This study aimed to develop a virtual imaging framework that simulates a new, investigational photon-counting CT (PCCT) system (NAEOTOM Alpha, Siemens). The simulator was built upon DukeSim and adapted to account for the attributes of the prototype. We validated the simulation platform against experimental measurements. The real and simulated images were quantitatively compared. The utility of our framework was demonstrated by three clinical applications. We successfully implemented the attributes of the PCCT prototype into DukeSim. Analysis suggested that lung lesion radiomics were more accurate with smaller pixel sizes and COPD quantifications were more accurate with thinner slices and softer kernels.
12031-61
Author(s): Patrick D. VanMeter, Jeffery Marsh, Kishore Rajendran, Shuai Leng, Cynthia McCollough, Mayo Clinic (United States)
In person: 23 February 2022 • 9:00 AM - 9:20 AM
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Coronary artery calcification is an important indicator of coronary disease. Accurate volume quantification of coronary calcification using computed tomography (CT) is challenging due to calcium blooming. In this study, ex-vivo coronary specimens were scanned on an investigational photon-counting detector (PCD) CT scanner and the estimated coronary calcification volume were compared with a conventional energy-integrating detector (EID) CT. An image-based denoising algorithm was applied to the PCD-CT images to achieve similar noise levels as EID-CT. Calcifications were segmented to estimate the volume, with micro-CT images of the same calcifications serving as reference. PCD-CT images showed reduced calcium blooming artifacts.
12031-62
Author(s): Juan C. R. Luna, Ian Harmon, Mini Das, Univ. of Houston (United States)
In person: 23 February 2022 • 9:20 AM - 9:40 AM
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X-ray image segmentation of different anatomical structures or tissue types is essential for diagnosing lesions of various kinds and for the differentiation between contrast agent and bone tissue. However, the complete separation of multiple targets and tissue types remains a challenge. We describe the use a combination of high-dimensional data clustering and material decomposition methods using spectral information from an energy resolving CdTe Medipix3 photon-counting detector. This paper introduces a flexible, iterative semi-supervised algorithm for multi-material decomposition that uses spectral measurements and the K-edge effects to label and classify CT voxel clusters using a Gaussian Mixture Model (GMM). Preliminary results show excellent quantitative accuracy and separation of more than 3 materials. Results are shown with phantom and mouse CT data. Our correction and calibration methods required for these successful decomposition results will also be described.
Session 13: CT Image Quality
In person: 23 February 2022 • 10:10 AM - 12:10 PM
Session Chairs: Lifeng Yu, Mayo Clinic (United States), Peter B. Noël, Univ. of Pennsylvania (United States)
12031-63
Author(s): Joshua R. Chen, Mang Feng, Ke Li, Univ. of Wisconsin-Madison (United States)
In person: 23 February 2022 • 10:10 AM - 10:30 AM
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In the past 15 years, much effort has been committed to lowering radiation dose for x-ray CT imaging due to the cancer risks associated with the ionizing radiation in CT. However, when the radiation dose in CT imaging is lowered, the CT number, crucial for diagnoses, becomes inaccurate and biased, opening the door for misdiagnosis, mistreatment, and other detrimental clinical consequences. The origin of the CT number bias addressed in this work is intrinsically rooted in the statistical nature of photons and the standard image formation process that has been used for the past 50 years in medical CT practices. We discovered a simple expression that accurately represents the bias from the stochastic nature of photons that can be easily implemented as a bias correction method. Our experimental results demonstrated that the proposed correction method not only addresses the CT number bias problem but also improves material quantification accuracy in spectral photon counting CT images.
12031-64
Author(s): Junyuan Li, Wenying Wang, Matthew Tivnan, Joseph W. Stayman, Grace J. Gang, Johns Hopkins Univ. (United States)
In person: 23 February 2022 • 10:30 AM - 10:50 AM
12031-65
Author(s): Viktor Haase, Siemens Healthcare GmbH (Germany), Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany); Frédéric Noo, The Univ. of Utah (United States); Karl Stierstorfer, Siemens Healthcare GmbH (Germany); Michael McNitt-Gray, Univ. of California, Los Angeles (United States)
In person: 23 February 2022 • 10:50 AM - 11:10 AM
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Measuring emphysema via non-contrast CT plays a major role in the assessment of COPD. We investigate the variability in lung CT attenuation values in terms of bias due to patient size and positioning. Experiments with an anthropomorphic lung phantom show that shifting the phantom off-center leads to asymmetry between the lungs. Also, extending the phantom with a fat-belt substantially increases the mean value and inhomogeneity in the lungs. To reduce these effects, we propose a patient-specific correction pipeline that works on the projection data, and we demonstrate reduced bias, which is expected to allow more robust emphysema scoring.
12031-66
Author(s): Scott S. Hsieh, Akitoshi Inoue, Parvathy Sudhir Pillai, Hao Gong, David R. Holmes, David A. Cook, Shuai Leng, Lifeng Yu, Rickey E. Carter, Joel G. Fletcher, Cynthia H. McCollough, Mayo Clinic (United States)
In person: 23 February 2022 • 11:10 AM - 11:30 AM
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Variability in radiologist performance could be better understood using unsupervised learning. After IRB approval, 25 radiologist readers read 40 portal phase liver CT exams, marking all metastases with a confidence rating. We formed a matrix of confidence ratings, with rows corresponding to readers and columns corresponding to metastases, with zeroes used for unmarked lesions. Using the clustergram, we identified a cluster of atypical lesions that were missed by several readers, and another cluster of subtle lesions where subspecialist radiologists were more confident of their diagnosis than trainee radiologists. These observations could be used to inform targeted training and education.
12031-67
Author(s): Wenying Wang, Matthew Tivnan, Junyuan Li, Joseph W. Stayman, Grace J. Gang, Johns Hopkins Univ. (United States)
In person: 23 February 2022 • 11:30 AM - 11:50 AM
12031-68
Author(s): Mingdong Fan, Zhongxing Zhou, Thomas Vrieze, Cynthia McCollough, Lifeng Yu, Mayo Clinic (United States)
In person: 23 February 2022 • 11:50 AM - 12:10 PM
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Channelized Hotelling observer (CHO), which has been shown to be well correlated with human observer performance in many clinical CT tasks, has a great potential to become the method of choice for objective image quality assessment. However, its use has been quite limited in routine CT practice due to lack of efficient implementation. In this work, a CHO model optimized for the most widely used ACR CT accreditation phantom was applied to evaluate the low-contrast detectability of a deep CNN-based denoising method. The CNN denoising showed non-inferior low-contrast detectability over IR at all object sizes and routine dose levels.
Session 14: Machine Learning in Imaging Physics
In person: 23 February 2022 • 1:20 PM - 3:00 PM
Session Chairs: Quanzheng Li, Massachusetts General Hospital (United States), Yuxiang Xing, Tsinghua Univ. (China)
12031-69
Author(s): Nathan R. Huber, Mayo Clinic (United States); Thomas Huber, Gustavus Adolphus College (United States); David Campeau, Hao Gong, Scott Hsieh, Shuai Leng, Lifeng Yu, Cynthia McCollough, Mayo Clinic (United States)
In person: 23 February 2022 • 1:20 PM - 1:40 PM
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This study introduces a framework to approximate the bias inflicted by CNN noise reduction of CT exams. First, CNN noise reduction was used to approximate the noise-free image and noise-only image of a CT scan. The noise and signal were then recombined with spatial decoupling to simulate an ensemble of 100 images. CNN noise reduction was applied to the simulated ensemble and pixel-wise bias calculated. This bias approximation technique was validated within natural images and phantoms. The technique was then tested on ten whole-body-low-dose CT (WBLD-CT) patient exams. Bias correction led to improved contrast of lung and bone structures.
12031-70
Author(s): Philip Trapp, Tim Vöth, Carlo Amato, Stefan Sawall, Marc Kachelrieß, German Cancer Research Ctr. (DKFZ) (Germany)
In person: 23 February 2022 • 1:40 PM - 2:00 PM
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Ring artifacts are a well-known problem in computed tomography (CT) and in particular in cone-beam CT (CBCT). This work addresses the reduction of ring artifacts in CT acquisitions using a data-driven approach. Deep convolutional neural networks (CNNs) of different dimensionalities are trained to estimate the ring artifacts directly from an uncorrected volume. This approach has the advantage that neither raw-data has to be available, nor any kind of resampling of the data is necessary. In addition to ring artifacts, our networks are also trained to correct for partial ring artifacts as they may occur in spiral CT or CBCT. This study shows that ring artifacts can be reduced in image domain by these neural networks. Our results suggest that a three-dimensional network is most suitable for this task.
12031-71
Author(s): Hao Gong, Zaki Ahmed, Jamison Thorne, Joel Fletcher, Cynthia McCollough, Shuai Leng, Mayo Clinic (United States)
In person: 23 February 2022 • 2:00 PM - 2:20 PM
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Conventional motion correction techniques are limited on patients with high / irregular heart rate, due to simplified modeling of CT systems and cardiac motion. Emerging deep learning based cardiac motion correction techniques have demonstrated the potential of further quality improvement. Yet, many methods request CT projection data or advanced motion simulation tools that are not readily available. We aim to develop an image-domain motion correction method, using convolutional neural network (CNN) integrated with customized attention and spatial transformer techniques. Slow temporal resolution (140ms) and fast temporal resolution (75ms) images were reconstructed to generate paired samples for training / testing. Different combinations of training-inference strategies and CNNs were evaluated. The CNN-based methods demonstrated the potential of improving image quality in cardiac exams with single-source CT.
12031-72
Author(s): Kaushik Dutta, Ziping Liu, Washington Univ. in St. Louis (United States); Richard Laforest, Washington Univ. in St Louis (United States); Abhinav Jha, Washington Univ. in St. Louis (United States); Kooresh Isaac Shoghi, Washington Univ. in St Louis (United States)
In person: 23 February 2022 • 2:20 PM - 2:40 PM
12031-73
Author(s): Yongfeng Gao, Stony Brook Univ. (United States); Ti Bai, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States); Shaojie Chang, Stony Brook Univ. (United States); Hao Zhang, Memorial Sloan-Kettering Cancer Ctr. (United States); Zhengrong Liang, Stony Brook Univ. (United States)
In person: 23 February 2022 • 2:40 PM - 3:00 PM
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The tissue specific MRF type texture prior (MRFt) proposed in our previous work has been demonstrated to be advantageous in various clinical tasks. However, this MRFt model requires a previous full-dose CT (FdCT) scan of the same patient to extract the texture information for LdCT reconstructions. This requirement may not be met in practice. To alleviate this limitation, we propose to build a MRFt generator by internalizing a database with paired FdCT and LdCT scans using a (conditional) encoder-decoder structure model. We denote this method as the MRFtG-ConED. This generation model depends only on physiological features thus is robust for ultra-low dose CT scans (i.e., dosage < 10mAs). When the dosage is not extremely low (i.e., dosage > 10mAs), some texture information from LdCT images reconstructed by filtered back projection (FBP) can be also used to provide extra information.
Session 15: Imaging Physics in Image-Guided Interventions: Joint Session with Conferences 12031 and 12034
In person: 23 February 2022 • 3:30 PM - 5:30 PM
Session Chair: Rebecca Fahrig, Siemens Healthineers (Germany)
12034-30
Author(s): Sepideh Hatamikia, ACMIT GmbH (Austria), Medizinische Univ. Wien (Austria); Ander Biguri, Univ. College London (United Kingdom); Gernot Kronreif, ACMIT GmbH (Austria); Joachim Kettenbach, Institut für Diagnostische und Interventionelle Radiologie und Nuklearmedizin, Landesklinikum (Austria); Tom Russ, Ruprecht-Karls-Univ. Heidelberg (Germany); Wolfgang Birkfellner, Medizinische Univ. Wien (Austria)
In person: 23 February 2022 • 3:30 PM - 3:50 PM
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Precise placement of needles plays a crucial role in percutaneous procedures as it helps to achieve higher diagnostic accuracy and accurate tumor targeting. C-arm cone-beam computed tomography (CBCT) has the potential to precisely image the anatomy in direct vicinity of the needle. However, exact needle positioning is very difficult due to strong metal artifacts around the needle. In this study, we evaluate the performance of the prior image constrained compressed sensing (PICCS) CBCT reconstruction in presence of metal objects. Our results confirm the high performance of PICCS to reduce needle artifacts using both circular and non-conventional trajectories under kinematic constraints.
12034-31
Author(s): Chih-Wei Chang, Yang Lei, Serdar Charyyev, Emory Univ. (United States); Shuai Leng, Mayo Clinic (United States); Tim Yoon, Jun Zhou, Xiaofeng Yang, Liyong Lin, Emory Univ. (United States)
In person: 23 February 2022 • 3:50 PM - 4:10 PM
12034-32
Author(s): Kevin Treb, Xu Ji, Mang Feng, Ran Zhang, Sarvesh Periyasamy, Paul Laeseke, Ke Li, Univ. of Wisconsin-Madison (United States)
In person: 23 February 2022 • 4:10 PM - 4:30 PM
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C-arm x-ray systems with flat panel detectors (FPDs) capable of cone-beam CT (CBCT) are suboptimal for low-contrast imaging tasks due to wide-beam geometry and limitations of FPDs. Photon counting detectors (PCDs) offer solutions to these limitations. To introduce narrow-beam PCD-CT to the interventional suite, we previously developed a prototype C-arm imaging system with a strip PCD. In this work, we present a data acquisition method to enlarge the z-coverage of the C-arm PCD-CT which involves back-and-forth gantry sweeps with automatic table translation for step-and-shoot acquisitions. The step-and-shoot C-arm PCD-CT improved low-contrast visibility and visualization of fine structures compared to FPD-CBCT.
12031-74
Author(s): Joseph F. Whitehead, Carson A. Hoffman, Paul F. Laeseke, Michael A. Speidel, Martin G. Wagner, Wisconsin Institutes for Medical Research (United States), Univ. of Wisconsin School of Medicine and Public Health (United States)
In person: 23 February 2022 • 4:30 PM - 4:50 PM
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A motion compensated quantitative digital subtraction angiography approach is presented which allows calculating blood flow velocities from 2D contrast-enhanced x-ray sequences with respiratory and cardiac motion. Phantom and animal studies were performed to evaluate the performance with and without motion compensation. The proposed technique could provide quantitative endpoints for interventional procedures, such as liver embolization, and could improve patient outcomes.
12031-75
Author(s): Tim Vöth, Ziehm Imaging GmbH (Germany), German Cancer Research Center (DKFZ) (Germany); Thomas König, Ziehm Imaging GmbH (Germany); Elias Eulig, Michael Knaup, German Cancer Research Center (DKFZ) (Germany); Klaus Hörndler, Ziehm Imaging GmbH (Germany); Marc Kachelrieß, German Cancer Research Center (DKFZ) (Germany)
In person: 23 February 2022 • 4:50 PM - 5:10 PM
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Today, 2D+T fluoroscopy is usually used for image guidance in interventional radiology. For challenging procedures, 4D (3D+T) image guidance would be advantageous. The difficulty in realizing X-ray-based 4D interventional guidance lies in the development of an extremely dose efficient reconstruction algorithm. To this end, we improve on a previously presented algorithm for the reconstruction of interventional tools. By incorporating temporal information into a 3D convolutional neural network, we reduce the number of X-ray projections that need to be acquired for the 3D reconstruction of guidewires from four to two, thereby halving dose and decreasing the demands put on imaging devices implementing the algorithm. In experiments with two moving guidewires in an anthropomorphic phantom, we observe little deviation of our 3D reconstructions from the ground truth.
12031-76
Author(s): Benjamin D. Killeen, Shreya Chakraborty, Greg Osgood, Mathias Unberath, Johns Hopkins Univ. (United States)
In person: 23 February 2022 • 5:10 PM - 5:30 PM
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During internal fixation of fractures, it is often challenging to safely position a K-wire due to the projective nature of X-ray images, especially in complex anatomy like the superior pubic ramus. This can result in excess acquisitions and repeat attempts. A perception-based algorithm that interprets interventional radiographs to infer the likelihood of cortical breach might reduce both. Here, we present first steps toward developing such an algorithm. We use an in silico strategy for collection of X-rays with and without cortical breach and demonstrate its suitability for machine learning by training an algorithm to detect cortical breach for fully-inserted K-wires.
Conference Chair
Stony Brook Univ. (United States)
Conference Chair
Mayo Clinic (United States)
Conference Co-Chair
Siemens Healthcare GmbH (Germany)
Program Committee
Univ. of California, Santa Cruz (United States)
Program Committee
Michigan State Univ. (United States)
Program Committee
UZ Leuven (Belgium)
Program Committee
KAIST (Korea, Republic of)
Program Committee
Univ. of Houston (United States)
Program Committee
KTH Royal Institute of Technology (Sweden)
Program Committee
Maria Drangova
Program Committee
Siemens Healthcare GmbH (Germany)
Program Committee
Varex Imaging Corp. (United States)
Program Committee
Program Committee
Marquette Univ. (United States)
Program Committee
U.S. Food and Drug Administration (United States), Univ. of Massachusetts Medical School (United States)
Program Committee
Deutsches Krebsforschungszentrum (Germany)
Program Committee
Program Committee
The Univ. of Chicago (United States)
Program Committee
Univ. of Wisconsin School of Medicine and Public Health (United States)
Program Committee
Massachusetts General Hospital (United States)
Program Committee
Duke Univ. (United States)
Program Committee
Univ. of Pennsylvania (United States)
Program Committee
Frédéric Noo
The Univ. of Utah (United States)
Program Committee
Univ. of California, Davis (United States)
Program Committee
GE Healthcare (United States)
Program Committee
Program Committee
National Institute of Biomedical Imaging and Bioengineering (United States)
Program Committee
Johns Hopkins Univ. (United States)
Program Committee
Lund Univ. (Sweden)
Program Committee
Stanford Univ. School of Medicine (United States)
Program Committee
Tsinghua Univ. (China)
Program Committee
Carestream Health, Inc. (United States)