Review on photoacoustic tomographic image reconstruction for acoustically heterogeneous tissues_Journal of Biomedical Engineering

Authors：

DUAN Shuang ,  SUN Zheng

Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, Hebei 071003, P.R.China;

Corresponding author：

Keywords：

photoacoustic tomography; image reconstruction; acoustic heterogeneity; acoustic attenuation

DOI：

10.7507/1001-5515.201809014

Video：

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Acoustic properties of biological tissues usually vary inhomogeneously in space. Tissues with different chemical composition often have different acoustic properties. The assumption of acoustic homogeneity may lead to blurred details, misalignment of targets and artifacts in the reconstructed photoacoustic tomography (PAT) images. This paper summarizes the main solutions to PAT imaging of acoustically heterogeneous tissues, including the variable sound speed and acoustic attenuation. The advantages and limits of the methods are discussed and the possible future development is prospected.

Citation： DUAN Shuang, SUN Zheng. Review on photoacoustic tomographic image reconstruction for acoustically heterogeneous tissues. Journal of Biomedical Engineering, 2019, 36(3): 486-492. doi: 10.7507/1001-5515.201809014 Copy

1.	Yao J, Wang L V. Recent progress in photoacoustic molecular imaging. Curr Opin Chem Biol, 2018, 45: 104-112.
2.	Wang L V, Gao L. Photoacoustic microscopy and computed tomography: from bench to bedside. Annu Rev Biomed Eng, 2014, 16(1): 155-185.
3.	Sun Zheng, Zheng Lan. Reconstruction of optical absorption coefficient distribution in intravascular photoacoustic imaging. Comput Biol Med, 2018, 97: 37-49.
4.	Huang Chao, Wang Kun, Nie Liming, et al. Full-wave iterative image reconstruction in photoacoustic tomography with acoustically inhomogeneous media. IEEE Trans Med Imaging, 2013, 32(6): 1097-1110.
5.	程任翔. 光声断层成象系统设计及其生物医学应用. 南京: 南京大学, 2017.
6.	Sun Zheng, Yuan Yuan, Han Duoduo. A computer-based simulator for intravascular photoacoustic images. Comput Biol Med, 2017, 81: 176-187.
7.	Qiao Wei, Chen Zhongjiang, Zhou Wangting, et al. All-optical photoacoustic Doppler transverse blood flow imaging. Opt Lett, 2018, 43(11): 2442-2445.
8.	Yang Chuanyao, Li Yuan, Yang Yanxue, et al. Multidimensional theranostics for tumor fluorescence imaging, photoacoustic imaging and photothermal treatment based on manganese doped carbon dots. J Biomed Nanotechnol, 2018, 14(9): 1590-1600.
9.	Qiao Yang, Gumin J, Maclellan C J, et al. Magnetic resonance and photoacoustic imaging of brain tumor mediated by mesenchymal stem cell labeled with multifunctional nanoparticle introduced via carotid artery injection. Nanotechnology, 2018, 29(16): 165101.
10.	Waibel D. Photoacoustic image reconstruction to solve the acoustic inverse problem with deep learning. Heidelberg: University of Heidelberg, 2018.
11.	Sun Zheng, Han Duoduo, Yuan Yuan. 2-D image reconstruction of photoacoustic endoscopic imaging based on time-reversal. Comput Biol Med, 2016, 76: 60-68.
12.	Wang Bingwen, Zhao Zhiqin, Liu Shuangli, et al. Mitigating acoustic heterogeneous effects in microwave-induced breast thermoacoustic tomography using multi-physical K-means clustering. Appl Phys Lett, 2017, 111(22): 223701.
13.	Ye Meng, Cao Meng, Feng Ting, et al. Automatic speed of sound correction with photoacoustic image reconstruction// Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging and Sensing. San Francisco, California, United States: 2016, 9708: 97083D.
14.	殷杰. 随机散射介质中的声波调控与光声成像研究. 南京: 南京大学, 2016.
15.	Stefanov P, Uhlmann G. Instability of the linearized problem in multiwave tomography of recovery both the source and the speed. Inverse Probl Imag, 2017, 7(4): 1367-1377.
16.	Liu Hongyu, Uhlmann G. Determining both sound speed and internal source in thermo- and photo-acoustic tomography. Inverse Probl, 2015, 31(10): 105005.
17.	Stefanov P, Yang Yang. Thermo and photoacoustic tomography with variable speed and planar detectors. SIAM J Math Anal, 2016, 49(1): 297-310.
18.	Zhang Hui, Chen Si, Wang Jiajun. The matrix transform for reconstruction on finite-element-based photoacoustic tomography. Journal of Computer & Communications, 2016, 4(3): 54-60.
19.	张辉. 基于有限元的光声图像重建算法研究. 苏州: 苏州大学, 2016.
20.	Cong Bing, Kondo K, Namita T, et al. Photoacoustic image quality enhancement by estimating mean sound speed based on optimum focusing. Jpn J Appl Phys, 2015, 54(7S1): 07HC13.
21.	Javaherian A, Holman S. A multi-grid iterative method for photoacoustic tomography. IEEE Trans Med Imaging, 2017, 36(3): 696-706.
22.	Haltmeier M, Nguyen L V. Analysis of iterative methods in photoacoustic tomography with variable sound speed. SIAM J Imag Sci, 2017, 10(2): 751-781.
23.	Zangerl G, Haltmeier M, Nguyen L V, et al. Full field inversion in photoacoustic tomography with variable sound speed. arXiv: 2018, 1808.00816.
24.	张弛, 汪源源. 声速不均匀介质的光声成像重建算法. 光学学报, 2008, 28(12): 2296-2301.
25.	Yan Xiaoheng, Zhang Ying, Liu Guoqiang, et al. Numerical calculation method for magneto-acoustic-electrical tomography of acoustically inhomogeneous tissues// Proceeding of 2017 International Seminar on Applied Physics, Optoelectronics and Photonics (APOP 2017), 2017: 461-467.
26.	Deán-Ben X L, Ma Rui, Razansky D, et al. Statistical approach for optoacoustic image reconstruction in the presence of strong acoustic heterogeneities. IEEE Trans Med Imaging, 2011, 30(2): 401-408.
27.	Deán-Ben X L, Ntziachristos V, Razansky D. Artefact reduction in optoacoustic tomographic imaging by estimating the distribution of acoustic scatterers. J Biomed Opt, 2012, 17(11): 110504.
28.	Deán-Ben X L, Ntziachristos V, Razansky D. Statistical optoacoustic image reconstruction using a-priori knowledge on the location of acoustic distortions. Appl Phys Lett, 2011, 98(17): 171110.
29.	Deánben X L, Ntziachristos V, Razansky D. Bayesian-based weighted optoacoustic tomographic reconstruction in acoustic scattering media//Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging and Sensing. San Francisco, California, United States: SPIE, 2013, 8581: 85811P.
30.	Deán-Ben X L, Ma Rui, Rosenthal A, et al. Weighted model-based optoacoustic reconstruction in acoustic scattering media. Phys Med Biol, 2013, 58(16): 5555-5566.
31.	谭毅, 邢达, 王毅, 等. 基于不同频率成份衰减矫正的光声成像方法. 光子学报, 2005, 34(7): 1019-1022.
32.	Poudel J, Matthews T P, Li Lei, et al. Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method. J Biomed Opt, 2017, 22(4): 41018.
33.	Haltmeier M, Kowar R, Nguyen L V. Iterative methods for photoacoustic tomography in attenuating acoustic media. Inverse Probl, 2017, 33(11): 115009.
34.	Scherzer O, Shi Cong. Reconstruction formulas for photoacoustic imaging in attenuating media. Inverse Probl, 2018, 34(1): 015006.
35.	Ding Lu, Deán-ben X L, Razansky D. 20 frames per second model-based reconstruction in cross-sectional optoacoustic tomography// Proceedings of SPIE Conference on Photons Plus Ultrasound: Imaging and Sensing. San Francisco, California, United States: SPIE, 2017, 10064: 100641A.
36.	Elbau P, Scherzer O, Shi Cong. Singular values of the attenuated photoacoustic imaging operator. J Differ Equ, 2017, 263(9): 5330-5376.
37.	Paltauf G, Hartmair P, Kovachev G, et al. Photoacoustic tomography with a line detector array// Proceedings of SPIE European Conference on Biomedical Optics 2017. Munich: Germany: SPIE, 2017, 10415: 1041509.
38.	Paltauf G, Hartmair P, Kovachev G, et al. Piezoelectric line detector array for photoacoustic tomography. Photoacoustics, 2017, 8(C): 28-36.
39.	Grün H, Berer T, Hochreiner A, et al. Photoacoustic imaging with integrating line detectors// Proceedings of SPIE Conference on Medical Imaging 2009: Ultrasonic Imaging and Signal Processing. Lake Buena Vista (Orlando Area), Florida, United States: SPIE, 2009, 7265: 72650K.
40.	Treeby B E, Zhang E Z, Cox B T. Photoacoustic tomography in absorbing acoustic media using time reversal. Inverse Probl, 2010, 26(11): 115003.
41.	Katsnelson V, Nguyen L V. On the convergence of the time reversal method for thermoacoustic tomography in elastic media. Appl Math Lett, 2018, 77: 79-86.
42.	Haltmeier M, Leitão A, Scherzer O. Kaczmarz methods for regularizing nonlinear ill-posed equations: I. Convergence analysis. Inverse Probl Imag, 2017, 1(2): 289-298.
43.	Vasin V V. Methods for solving nonlinear ill-posed problems based on the Tikhonov-Lavrentiev regularization and iterative approximation. Institute of Mathematics & Mechanics, 2016, 4(4): 60-73.
44.	Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
45.	Shen Dinggang, Wu Guorong, Suk H I. Deep learning in medical image analysis. Annu Rev Biomed Eng, 2017, 19(1): 221-248.
46.	Allman D, Reiter A, Bell M A. Photoacoustic source detection and reflection artifact removal enabled by deep learning. IEEE Trans Med Imaging, 2018, 37(6, SI): 1464-1477.
47.	刘飞, 张俊然, 杨豪. 基于深度学习的医学图像识别研究进展. 中国生物医学工程学报, 2018, 37(1): 86-94.
48.	Hauptmann A, Lucka F, Betcke M, et al. Model based learning for accelerated, limited-view 3D photoacoustic tomography. IEEE Trans Med Imaging, 2017, 37(6): 1382-1393.
49.	Abe H, Shiina T. Focusing light within scattering media for photoacoustic tomography in a limited-view-angle tomography setting// 2014 IEEE International Ultrasonics Symposium. Chicago, IL, USA: IEEE, 2014: 1273-1276.
50.	Schwab J, Antholzer S, Nuster R, et al. DALnet: High-resolution photoacoustic projection imaging using deep learning. arXiv: 2018, 1801.06693.
51.	Antholzer S, Haltmeier M, Schwab J. Deep learning for photoacoustic tomography from sparse data. arXiv: 2017, 1704.04587.

1. Yao J, Wang L V. Recent progress in photoacoustic molecular imaging. Curr Opin Chem Biol, 2018, 45: 104-112.
2. Wang L V, Gao L. Photoacoustic microscopy and computed tomography: from bench to bedside. Annu Rev Biomed Eng, 2014, 16(1): 155-185.
3. Sun Zheng, Zheng Lan. Reconstruction of optical absorption coefficient distribution in intravascular photoacoustic imaging. Comput Biol Med, 2018, 97: 37-49.
4. Huang Chao, Wang Kun, Nie Liming, et al. Full-wave iterative image reconstruction in photoacoustic tomography with acoustically inhomogeneous media. IEEE Trans Med Imaging, 2013, 32(6): 1097-1110.
5. 程任翔. 光声断层成象系统设计及其生物医学应用. 南京: 南京大学, 2017.
6. Sun Zheng, Yuan Yuan, Han Duoduo. A computer-based simulator for intravascular photoacoustic images. Comput Biol Med, 2017, 81: 176-187.
7. Qiao Wei, Chen Zhongjiang, Zhou Wangting, et al. All-optical photoacoustic Doppler transverse blood flow imaging. Opt Lett, 2018, 43(11): 2442-2445.
8. Yang Chuanyao, Li Yuan, Yang Yanxue, et al. Multidimensional theranostics for tumor fluorescence imaging, photoacoustic imaging and photothermal treatment based on manganese doped carbon dots. J Biomed Nanotechnol, 2018, 14(9): 1590-1600.
9. Qiao Yang, Gumin J, Maclellan C J, et al. Magnetic resonance and photoacoustic imaging of brain tumor mediated by mesenchymal stem cell labeled with multifunctional nanoparticle introduced via carotid artery injection. Nanotechnology, 2018, 29(16): 165101.
10. Waibel D. Photoacoustic image reconstruction to solve the acoustic inverse problem with deep learning. Heidelberg: University of Heidelberg, 2018.
11. Sun Zheng, Han Duoduo, Yuan Yuan. 2-D image reconstruction of photoacoustic endoscopic imaging based on time-reversal. Comput Biol Med, 2016, 76: 60-68.
12. Wang Bingwen, Zhao Zhiqin, Liu Shuangli, et al. Mitigating acoustic heterogeneous effects in microwave-induced breast thermoacoustic tomography using multi-physical K-means clustering. Appl Phys Lett, 2017, 111(22): 223701.
13. Ye Meng, Cao Meng, Feng Ting, et al. Automatic speed of sound correction with photoacoustic image reconstruction// Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging and Sensing. San Francisco, California, United States: 2016, 9708: 97083D.
14. 殷杰. 随机散射介质中的声波调控与光声成像研究. 南京: 南京大学, 2016.
15. Stefanov P, Uhlmann G. Instability of the linearized problem in multiwave tomography of recovery both the source and the speed. Inverse Probl Imag, 2017, 7(4): 1367-1377.
16. Liu Hongyu, Uhlmann G. Determining both sound speed and internal source in thermo- and photo-acoustic tomography. Inverse Probl, 2015, 31(10): 105005.
17. Stefanov P, Yang Yang. Thermo and photoacoustic tomography with variable speed and planar detectors. SIAM J Math Anal, 2016, 49(1): 297-310.
18. Zhang Hui, Chen Si, Wang Jiajun. The matrix transform for reconstruction on finite-element-based photoacoustic tomography. Journal of Computer & Communications, 2016, 4(3): 54-60.
19. 张辉. 基于有限元的光声图像重建算法研究. 苏州: 苏州大学, 2016.
20. Cong Bing, Kondo K, Namita T, et al. Photoacoustic image quality enhancement by estimating mean sound speed based on optimum focusing. Jpn J Appl Phys, 2015, 54(7S1): 07HC13.
21. Javaherian A, Holman S. A multi-grid iterative method for photoacoustic tomography. IEEE Trans Med Imaging, 2017, 36(3): 696-706.
22. Haltmeier M, Nguyen L V. Analysis of iterative methods in photoacoustic tomography with variable sound speed. SIAM J Imag Sci, 2017, 10(2): 751-781.
23. Zangerl G, Haltmeier M, Nguyen L V, et al. Full field inversion in photoacoustic tomography with variable sound speed. arXiv: 2018, 1808.00816.
24. 张弛, 汪源源. 声速不均匀介质的光声成像重建算法. 光学学报, 2008, 28(12): 2296-2301.
25. Yan Xiaoheng, Zhang Ying, Liu Guoqiang, et al. Numerical calculation method for magneto-acoustic-electrical tomography of acoustically inhomogeneous tissues// Proceeding of 2017 International Seminar on Applied Physics, Optoelectronics and Photonics (APOP 2017), 2017: 461-467.
26. Deán-Ben X L, Ma Rui, Razansky D, et al. Statistical approach for optoacoustic image reconstruction in the presence of strong acoustic heterogeneities. IEEE Trans Med Imaging, 2011, 30(2): 401-408.
27. Deán-Ben X L, Ntziachristos V, Razansky D. Artefact reduction in optoacoustic tomographic imaging by estimating the distribution of acoustic scatterers. J Biomed Opt, 2012, 17(11): 110504.
28. Deán-Ben X L, Ntziachristos V, Razansky D. Statistical optoacoustic image reconstruction using a-priori knowledge on the location of acoustic distortions. Appl Phys Lett, 2011, 98(17): 171110.
29. Deánben X L, Ntziachristos V, Razansky D. Bayesian-based weighted optoacoustic tomographic reconstruction in acoustic scattering media//Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging and Sensing. San Francisco, California, United States: SPIE, 2013, 8581: 85811P.
30. Deán-Ben X L, Ma Rui, Rosenthal A, et al. Weighted model-based optoacoustic reconstruction in acoustic scattering media. Phys Med Biol, 2013, 58(16): 5555-5566.
31. 谭毅, 邢达, 王毅, 等. 基于不同频率成份衰减矫正的光声成像方法. 光子学报, 2005, 34(7): 1019-1022.
32. Poudel J, Matthews T P, Li Lei, et al. Mitigation of artifacts due to isolated acoustic heterogeneities in photoacoustic computed tomography using a variable data truncation-based reconstruction method. J Biomed Opt, 2017, 22(4): 41018.
33. Haltmeier M, Kowar R, Nguyen L V. Iterative methods for photoacoustic tomography in attenuating acoustic media. Inverse Probl, 2017, 33(11): 115009.
34. Scherzer O, Shi Cong. Reconstruction formulas for photoacoustic imaging in attenuating media. Inverse Probl, 2018, 34(1): 015006.
35. Ding Lu, Deán-ben X L, Razansky D. 20 frames per second model-based reconstruction in cross-sectional optoacoustic tomography// Proceedings of SPIE Conference on Photons Plus Ultrasound: Imaging and Sensing. San Francisco, California, United States: SPIE, 2017, 10064: 100641A.
36. Elbau P, Scherzer O, Shi Cong. Singular values of the attenuated photoacoustic imaging operator. J Differ Equ, 2017, 263(9): 5330-5376.
37. Paltauf G, Hartmair P, Kovachev G, et al. Photoacoustic tomography with a line detector array// Proceedings of SPIE European Conference on Biomedical Optics 2017. Munich: Germany: SPIE, 2017, 10415: 1041509.
38. Paltauf G, Hartmair P, Kovachev G, et al. Piezoelectric line detector array for photoacoustic tomography. Photoacoustics, 2017, 8(C): 28-36.
39. Grün H, Berer T, Hochreiner A, et al. Photoacoustic imaging with integrating line detectors// Proceedings of SPIE Conference on Medical Imaging 2009: Ultrasonic Imaging and Signal Processing. Lake Buena Vista (Orlando Area), Florida, United States: SPIE, 2009, 7265: 72650K.
40. Treeby B E, Zhang E Z, Cox B T. Photoacoustic tomography in absorbing acoustic media using time reversal. Inverse Probl, 2010, 26(11): 115003.
41. Katsnelson V, Nguyen L V. On the convergence of the time reversal method for thermoacoustic tomography in elastic media. Appl Math Lett, 2018, 77: 79-86.
42. Haltmeier M, Leitão A, Scherzer O. Kaczmarz methods for regularizing nonlinear ill-posed equations: I. Convergence analysis. Inverse Probl Imag, 2017, 1(2): 289-298.
43. Vasin V V. Methods for solving nonlinear ill-posed problems based on the Tikhonov-Lavrentiev regularization and iterative approximation. Institute of Mathematics & Mechanics, 2016, 4(4): 60-73.
44. Litjens G, Kooi T, Bejnordi B E, et al. A survey on deep learning in medical image analysis. Med Image Anal, 2017, 42: 60-88.
45. Shen Dinggang, Wu Guorong, Suk H I. Deep learning in medical image analysis. Annu Rev Biomed Eng, 2017, 19(1): 221-248.
46. Allman D, Reiter A, Bell M A. Photoacoustic source detection and reflection artifact removal enabled by deep learning. IEEE Trans Med Imaging, 2018, 37(6, SI): 1464-1477.
47. 刘飞, 张俊然, 杨豪. 基于深度学习的医学图像识别研究进展. 中国生物医学工程学报, 2018, 37(1): 86-94.
48. Hauptmann A, Lucka F, Betcke M, et al. Model based learning for accelerated, limited-view 3D photoacoustic tomography. IEEE Trans Med Imaging, 2017, 37(6): 1382-1393.
49. Abe H, Shiina T. Focusing light within scattering media for photoacoustic tomography in a limited-view-angle tomography setting// 2014 IEEE International Ultrasonics Symposium. Chicago, IL, USA: IEEE, 2014: 1273-1276.
50. Schwab J, Antholzer S, Nuster R, et al. DALnet: High-resolution photoacoustic projection imaging using deep learning. arXiv: 2018, 1801.06693.
51. Antholzer S, Haltmeier M, Schwab J. Deep learning for photoacoustic tomography from sparse data. arXiv: 2017, 1704.04587.

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