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Biomedical Optics & Medical Imaging

Advanced image reconstruction for ultrasound-photoacoustic computed tomography

Novel, low-complexity computation methods enable reconstruction of high spatial resolution images for combined ultrasound-photoacoustic computed tomography.
23 February 2015, SPIE Newsroom. DOI: 10.1117/2.1201502.005767

Photoacoustic computed tomography (PACT), also known as optoacoustic tomography, is an emerging soft-tissue imaging modality that has great potential for a wide range of biomedical applications. It can be viewed as a hybrid imaging technique that combines an optical contrast mechanism with ultrasonic detection principles. In this way, it combines the advantages of optical and ultrasonic imaging while circumventing their primary limitations. The goal of PACT is to produce an estimate of the spatially variant absorbed optical energy density within an object.

Purchase SPIE Field Guide to MicroscopyAs with other computed imaging modalities, such as x-ray computed tomography and magnetic resonance imaging, PACT image formation uses a reconstruction algorithm.1 Conventional PACT reconstruction methods assume that the object and surrounding medium are described by a constant speed-of-sound (SOS) value. To accurately recover fine structures, it is necessary to quantify and compensate for SOS heterogeneities during PACT image reconstruction. Several groups have sought to achieve this by proposing hybrid systems that combine PACT with ultrasound computed tomography (USCT),2–4 reconstructing an SOS map using USCT and then using this to inform the PACT reconstruction method. Additionally, the SOS map can provide structural information regarding tissue5 that is complementary to the functional information provided by PACT.

With leading instrument builders, we are developing advanced image reconstruction methods for PACT and USCT. In collaboration with Lihong Wang, we are developing transcranial PACT brain imaging and ultrasound-informed methods for small animal PACT imaging applications. An important goal of this research is to improve PACT image quality by compensating for acoustic aberrations that result from bones and/or gas pockets.6 In another project, we are working with Alexander Oraevsky and his team at TomoWave Laboratories Inc. to develop ultrasound-informed reconstruction methods for imaging both the breast and small animals. Figure 1 shows a cross-sectional PACT image of a mouse and the corresponding SOS map. The TomoWave team has developed a prototype system for human breast imaging. This is currently under evaluation at MD Anderson Cancer Center, where clinical data will validate and refine our image reconstruction algorithms.


Figure 1. Left: Photoacoustic computed tomography (PACT) image of a section through a mouse abdomen. Right: Speed-of-sound image of the same slice. (Data acquired by TomoWave Laboratories Inc.)

Recently, our team collaborated with Neb Duric and his colleagues at Delphinus Medical Technologies Inc. on the development of waveform-based methods for SOS reconstruction in breast imaging applications.7 Unlike conventional bent-ray methods, which ignore diffraction effects, waveform-based reconstruction methods invert the acoustic wave equation and can therefore produce images with substantially enhanced spatial resolution. There have been few investigations of waveform-based reconstruction methods for medical ultrasound tomography applications, due to their extreme computational burden. By use of source encoding concepts and a stochastic optimization framework, our reconstruction method has circumvented this by reducing the computational complexity by two orders of magnitude.7 When source encoding is employed, all acoustic sources are encoded and virtually fired simultaneously. We combine the measured wave field data in the same way. As a result, we can estimate the sound speed distribution by solving a stochastic optimization problem with an efficient online learning algorithm. Figure 2 shows USCT images of an experimental breast phantom reconstructed using algorithms based on standard bent rays and on our new waveform inversion technique.


Figure 2. Images of a breast phantom reconstructed using algorithms based on bent rays (left) and waveforms (right). (Data acquired by Delphinus Medical Technologies Inc.)

An important and interesting question is whether it is possible to accurately determine both the absorbed optical energy density distribution and the SOS map from the measured PACT data alone. Existing theoretical and computational studies of this problem are limited in scope.8–11 Therefore, we developed and investigated an approach to attempt the simultaneous reconstruction of these quantities from PACT data alone.12 We found that joint reconstruction of the absorbed optical energy density and the SOS map from just the PACT data is numerically unstable, and therefore unlikely to be highly effective in practical applications where noise and other errors contaminate the measurements.

Our current work proposes a shift in the way that images are reconstructed in the hybrid PACT-USCT approach. Inspired by our observation that information about the SOS distribution is encoded in PACT measurements, we propose to jointly reconstruct the absorbed optical energy density and SOS distributions from a combined set of USCT and PACT measurements, thereby reducing the two reconstruction problems into one. This has several advantages over conventional approaches, in which PACT and USCT images are reconstructed independently. First, variations in the SOS are automatically accounted for, optimizing PACT image quality. Second, the reconstructed PACT and USCT images possess minimal systematic artifacts because errors in the imaging models are optimally balanced during the joint reconstruction. Third, our approach exploits information regarding the SOS distribution in the full-view PACT data, and therefore enables high-resolution reconstruction of the SOS distribution from sparse array USCT data.

In summary, both PACT and USCT are emerging as powerful bioimaging modalities that can benefit a wide range of biomedical and clinical applications. They can be implemented using common hardware, thereby permitting multi-modality imaging that exploits optical and ultrasound contrast mechanisms. Moreover, information about an object's acoustic properties revealed by USCT can enable improved PACT image quality. In the future, our team will develop image reconstruction methodologies for multi-modality PACT-USCT imaging that can improve its effectiveness and promote its widespread use.

This work was supported in part by National Institutes of Health awards EB016963, EB010049, and CA167446.


Mark Anastasio, Kun Wang
Department of Biomedical Engineering
Washington University in St. Louis
St. Louis, MO

Mark Anastasio was on the faculty at Illinois Institute of Technology from 2001 to 2010, and is currently a professor of biomedical engineering. He has conducted pioneering research in the fields of PACT, diffraction tomography, and x-ray phase-contrast imaging.

Kun Wang is a staff scientist and has conducted research on imaging system modeling/optimization and algorithm development for photoacoustic and ultrasound computed tomography.


References:
1. K. Wang, R. Su, A. A. Oraevsky, M. A. Anastasio, Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography, Phys. Med. Biol. 57(17), p. 5399, 2012.
2. X. Jin, L. V. Wang, Thermoacoustic tomography with correction for acoustic speed variations, Phys. Med. Biol. 51(24), p. 6437, 2006.
3. S. Manohar, R. G. H. Willemink, F. van der Heijden, C. H. Slump, T. G. van Leeuwen, Concomitant speed-of-sound tomography in photoacoustic imaging, Appl. Phys. Lett. 91(13), p. 131911, 2007.
4. F. Anis, R. Su, V. V. Nadvoretsky, A. Conjusteau, S. A. Ermilov, A. A. Oraevsky, M. A. Anastasio, Image reconstruction and system optimization for three-dimensional speed of sound tomography using laser-induced ultrasound, Proc. SPIE 8581, p. 85814T, 2013. doi:10.1117/12.2008813
5. N. Duric, P. Littrup, A. Babkin, D. Chambers, S. Azevedo, A. Kalinin, R. Pevzner, et al., Development of ultrasound tomography for breast imaging: technical assessment, Med. Phys. 32(5), p. 1375-1386, 2005.
6. J. Xia, C. Huang, K. Maslov, M. A. Anastasio, L. V. Wang, Enhancement of photoacoustic tomography by ultrasonic computed tomography based on optical excitation of elements of a full-ring transducer array, Opt. Lett. 38(16), p. 3140-3143, 2013.
7. K. Wang, T. Matthews, F. Anis, C. Li, N. Duric, M. Anastasio, Waveform inversion with source encoding for breast sound speed reconstruction in ultrasound computed tomography, IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., 2015. (In press.)
8. J. Zhang, K. Wang, Y. Yang, M. A. Anastasio, Simultaneous reconstruction of speed-of-sound and optical absorption properties in photoacoustic tomography via a time-domain iterative algorithm, Proc. SPIE 6856, p. 68561F, 2008. doi:10.1117/12.764171
9. H. Jiang, Z. Yuan, X. Gu, Spatially varying optical and acoustic property reconstruction using finite-element-based photoacoustic tomography, J. Opt. Soc. Am. A 23(4), p. 878-888, 2006.
10. G. Chen, X. Wang, J. Wang, Z. Zhao, Z. Nie, Q. Liu, TR adjoint imaging method for MITAT, Prog. Electromagn. Res. B 46, p. 41-57, 2013.
11. K. S. Hickmann, Unique Determination of Acoustic Properties from Thermoacoustic Data, PhD thesis, Oregon State University, 2010.
12. C. Huang, K. Wang, R. W. Schoonover, L. V. Wang, M. A. Anastasio, Simultaneous reconstruction of absorbed optical energy density and speed of sound distributions in photoacoustic computed tomography, Proc. SPIE 8943, p. 894360, 2014. doi:10.1117/12.2041825