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Proceedings Paper

Leveraging deep generative model for direct energy-resolving CT imaging via existing energy-integrating CT images
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Paper Abstract

Energy-resolving CT (ErCT) with a photon counting detector (PCD) is able to generate multi-energy data with high spatial resolution, and it can be used to improve contrast-to-noise ratio (CNR) of iodinated tissues and to reduce beam hardening artifacts. In addition, ErCT allows for generating virtual mono-energetic CT images with improved CNR. However, most of ErCT scanners are lab-built, but little used in clinical research. Deep learning based methods can help to generate ErCT images from energy-integrating CT (EiCT) images via convolution neural networks (CNNs) because of its capability in learning features of the EiCT images and ErCT images. Nevertheless, current CNNs usually generate ErCT images at one energy bin at a time, and there is large room for improvement, such as, generating multi-energy ErCT images at a time. Therefore, in this work, we investigate to leverage a deep generative model (IuGAN-ErCT) to simultaneously generate ErCT images at multiple energy bins from existing EiCT images. Specifically, a unified generative adversarial network (GAN) is employed. With a single generator, the generative network learns the latent correlation between the EiCT images and ErCT images to estimate ErCT images from EiCT images. Moreover, to maintain the value accuracy of different ErCT images, we introduced a fidelity loss function. In the experiment, 1384 abdomen and chest images collected from 22 patients were utilized to train the proposed IuGAN-ErCT method and 130 slices were used for test. Result shows that the IuGAN-ErCT method can generate more accurate ErCT images than the uGAN-ErCT method both in quantitative and qualitative evaluation.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124U (16 March 2020); doi: 10.1117/12.2548992
Show Author Affiliations
Lisha Yao, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Sui Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Danyang Li, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Manman Zhu, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Qi Gao, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Shanli Zhang, The First Affiliated Hospital of Guangzhou Univ. of Traditional Chinese Medicine (China)
Zhaoying Bian, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Jing Huang, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)
Dong Zeng, South China Univ. of Technology (China)
Jianhua Ma, Southern Medical Univ. (China)
Guangzhou Key Lab. of Medical Radiation Imaging and Detection Technology (China)


Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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