Progress of motion artifact correction in photoacoustic microscopy and photoacoustic tomography_Journal of Biomedical Engineering

Authors：

DU Jiejie ^1,2 ,  SUN Zheng ^1,2

1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, Hebei 071003, P.R.China;
2. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding, Hebei 071003, P.R.China;

Corresponding author：

Keywords：

photoacoustic imaging; photoacoustic microscopy; photoacoustic tomography; motion artifact; motion correction

DOI：

10.7507/1001-5515.202009062

Video：

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Photoacoustic imaging (PAI) is a rapidly developing hybrid biomedical imaging technology, which is capable of providing structural and functional information of biological tissues. Due to inevitable motion of the imaging object, such as respiration, heartbeat or eye rotation, motion artifacts are observed in the reconstructed images, which reduce the imaging resolution and increase the difficulty of obtaining high-quality images. This paper summarizes current methods for correcting and compensating motion artifacts in photoacoustic microscopy (PAM) and photoacoustic tomography (PAT), discusses their advantages and limits and forecasts possible future work.

Citation： DU Jiejie, SUN Zheng. Progress of motion artifact correction in photoacoustic microscopy and photoacoustic tomography. Journal of Biomedical Engineering, 2021, 38(2): 369-378. doi: 10.7507/1001-5515.202009062 Copy

1.	孙梦娟, 吴丹, 蒋华北. 光声成像与中医药研究. 世界中医药, 2020, 15(11): 1527-1534.
2.	林日强, 冷吉, 陈敬钦, 等. 面向临床应用的光声成像技术. 中国医疗设备, 2018, 33(1): 1-5, 15.
3.	Zhao H, Chen N, Li T, et al. Motion correction in optical resolution photoacoustic microscopy. IEEE Trans Med Imaging, 2019, 38(9): 2139-2150.
4.	Lin R, Chen J, Wang H, et al. Longitudinal label-free optical-resolution photoacoustic microscopy of tumor angiogenesis in vivo. Quant Imag Med Surg, 2015, 5: 23.
5.	Zhao H, Wang G, Lin R, et al. Three-dimensional hessian matrix-based quantitative vascular imaging of rat iris with optical-resolution photoacoustic microscopy in vivo. J Biomed Opt, 2018, 23(4): 1-11.
6.	Zhao H, Li K, Chen N, et al. Multiscale vascular enhancement filter applied to in vivo morphologic and functional photoacoustic imaging of rat ocular vasculature. IEEE Photonics J, 2019, 11(6): 3900912.
7.	龙晓云, 田超. 生物医学光声显微成像: 技术和应用进展. 中国激光, 2020, 47(2): 207-221.
8.	张建辉, 陈宁波, 王柏权, 等. 光声显微成像技术的研究进展. 数据采集与处理, 2019, 34(5): 771-788.
9.	陈懂. 声学分辨率光声显微成像系统的构建与研究. 昆明: 云南大学, 2018.
10.	杜怡. 光声显微成像信号的时频分析和去噪. 上海: 上海交通大学, 2015.
11.	Shintate R, Morino T, Kawaguchi K, et al. Development of optical resolution photoacoustic microscopy with sub-micron lateral resolution for visualization of cells and their structures. Jpn J Appl Phys, 2020, 59(SK): 1-11.
12.	Bailleul J, Simon B, Debailleul M, et al. Tomographic diffractive microscopy: towards high-resolution 3-D real-time data acquisition, image reconstruction and display of unlabeled samples. Opt Commun, 2018, 422(SI): 28-37.
13.	Liu W, Yao J J. Photoacoustic microscopy: principles and biomedical applications. Biomed Eng Lett, 2018, 8(2): 203-213.
14.	Schwarz M, Garzorz-Stark N, Eyerich K, et al. Motion correction in optoacoustic mesoscopy. Sci Rep, 2017, 7(1): 10386.
15.	Aguirre J, Berezhnoi A, He H L, et al. Motion quantifification and automated correction in clinical RSOM. IEEE Trans Med Imag, 2019, 38(6): 1340-1346.
16.	Cheng Z W, Ma H G, Wang Z Y, et al. Subpixel and on-line motion correction for photoacoustic dermoscopy. IEEE J Sel Top Quantum Electron, 2021, 27(4): 6800408.
17.	Lan S, Guo Z, You J. Non-rigid medical image registration using image field in demons algorithm. Pattern Recogn Lett, 2019, 125(7): 98-104.
18.	Lin L H, Chen T J. Mutual information correlation with human vision in medical image compression. Curr Med Imaging Rev, 2018, 14(1): 64-70.
19.	Moothanchery M, Bi R, Kim J Y, et al. Optical resolution photoacoustic microscopy based on multimode fibers. Biomed Opt Express, 2018, 9(3): 1190-1197.
20.	孙正, 王新宇. 深度学习在光声成像中的应用现状. 计算机科学, 2020, 47(6A): 148-152.
21.	Chen X X, Qi W Z, Xi L. Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy. Vis Comput Ind Biomed Art, 2019, 2(1): 1-6.
22.	孙正, 闫向阳. 采用稀疏测量数据的有限角度光声层析成像的研究进展. 声学技术, 2020, 39(1): 1-10.
23.	刘强, 金天, 陈倩, 等. 小型化光声成像技术在生物医学领域的研究进展. 中国激光, 2020, 47(2): 256-264.
24.	Deán-Ben X L, Bay E. Functional optoacoustic imaging of moving objects using microsecond-delay acquisition of multispectral three-dimensional tomographic data. Sci Rep, 2014, 4: 5878.
25.	Märk J, Wagener A. Photoacoustic pump-probe tomography of fluorophores in vivo using interleaved image acquisition for motion suppression. Sci Rep, 2017, 7: 40496.
26.	Lu Y H, Fontaine K, Mulnix T, et al. Respiratory motion compensation for PET/CT with motion information derived from matched attenuation-corrected gated PET data. J Nucl Med, 2018, 59(9): 1480-1486.
27.	Moghari M H, Barthur A, Amaral M E, et al. Free-breathing whole-heart 3D cine magnetic resonance imaging with prospective respiratory motion compensation. Magn Reson Med, 2018, 80(1): 181-189.
28.	Ron A, Davoudi N, Deán-Ben X L, et al. Self-gated respiratory motion rejection for optoacoustic tomography. Appl Sci, 2019, 9(13): 2737.
29.	Xia J, Chen W Y, Maslov K, et al. Retrospective respiration-gated whole-body photoacoustic computed tomography of mice. J Biomed Opt, 2014, 19(1): 16003.
30.	van de Lindt T, Fast M, Nowee M, et al. PO-0956: A self-gated coronal 4D-MRI method for daily imaging of liver lesions on an MR-Linac. Radiother Oncol, 2018, 127(S5): 23-24.
31.	Capo M, Perez A, Lozano J A. A cheap feature selection approach for the k-means algorithm. IEEE T Neur Net Lear, 2020. DOI: 10.1109/TNNLS.2020.3002576.
32.	马健. 基于改进的k-means算法初始化方法研究. 云南民族大学学报(自然科学版), 2020, 29(3): 274-278.
33.	Karlas A, Kallmayer M, Fasoula N A, et al. Multispectral optoacoustic tomography of muscle perfusion and oxygenation under arterial and venous occlusion: a human pilot study. J Biophotonics, 2020, 13(6): 1-9.
34.	Taruttis A, Claussen J, Razansky D, et al. Motion clustering for deblurring multispectral optoacoustic tomography images of the mouse heart. J Biomed Opt, 2012, 17(1): 016009.
35.	Ivankovic I, Merčep E, Schmedt, C G, et al. Real-time volumetric assessment of the human carotid artery: handheld multispectral optoacoustic tomography. Radiology, 2019, 291(1): 45-50.
36.	Chung J, Nguyen L. Motion estimation and correction in photoacoustic tomographic reconstruction. SIAM J Imaging Sci, 2017, 10(1): 216-242.
37.	Gubinelli M, Koch H, Oh T. Renormalization of the two-dimensional stochastic nonlinear wave equation. T Am Math Soc, 2018, 370(10): 7335-7359.
38.	Schuster T, Hahn B, Burger M. Dynamic inverse problems: modelling-regularization-numerics. Inverse Probl, 2018, 34(4): 040301.
39.	Chen G Y, Wang S Q, Wang D Q, et al. Regularization methods for separable nonlinear models. Nonlinear Dyn, 2019, 98(2): 1287-1298.
40.	Iakymchuk R, Yarmola H, Shakhno S. Gauss-Newton-Secant method for solving nonlinear least squares problems. Proc Appl Math Mech, 2018, 18(1): e201800228.
41.	Kim M, Kang J, Chang J H, et al. Image quality improvement based on inter-frame motion compensation for photoacoustic imaging: a preliminary study// Proceedings of 2013 IEEE International Ultrasonics Symposium (IUS). Prague: IEEE, 2013: 1527-1531.
42.	Manikandan L C, Nair S A H, Sanalkumar K P, et al. A study and analysis on block matching algorithms for motion estimation in video coding. Cluster Comput, 2019, 22(5): 11773-11780.
43.	Miranda C, Marschall E, Browning B, et al. Side-viewing photoacoustic waveguide endoscopy. Photoacoustics, 2020, 19: 100167.
44.	Iskander-Rizk S, Wu M, Springeling G, et al. In vivo intravascular photoacoustic imaging of lipid plaque in coronary atherosclerosis. EuroIntervention, 2019, 15(5): 452-456.

1. 孙梦娟, 吴丹, 蒋华北. 光声成像与中医药研究. 世界中医药, 2020, 15(11): 1527-1534.
2. 林日强, 冷吉, 陈敬钦, 等. 面向临床应用的光声成像技术. 中国医疗设备, 2018, 33(1): 1-5, 15.
3. Zhao H, Chen N, Li T, et al. Motion correction in optical resolution photoacoustic microscopy. IEEE Trans Med Imaging, 2019, 38(9): 2139-2150.
4. Lin R, Chen J, Wang H, et al. Longitudinal label-free optical-resolution photoacoustic microscopy of tumor angiogenesis in vivo. Quant Imag Med Surg, 2015, 5: 23.
5. Zhao H, Wang G, Lin R, et al. Three-dimensional hessian matrix-based quantitative vascular imaging of rat iris with optical-resolution photoacoustic microscopy in vivo. J Biomed Opt, 2018, 23(4): 1-11.
6. Zhao H, Li K, Chen N, et al. Multiscale vascular enhancement filter applied to in vivo morphologic and functional photoacoustic imaging of rat ocular vasculature. IEEE Photonics J, 2019, 11(6): 3900912.
7. 龙晓云, 田超. 生物医学光声显微成像: 技术和应用进展. 中国激光, 2020, 47(2): 207-221.
8. 张建辉, 陈宁波, 王柏权, 等. 光声显微成像技术的研究进展. 数据采集与处理, 2019, 34(5): 771-788.
9. 陈懂. 声学分辨率光声显微成像系统的构建与研究. 昆明: 云南大学, 2018.
10. 杜怡. 光声显微成像信号的时频分析和去噪. 上海: 上海交通大学, 2015.
11. Shintate R, Morino T, Kawaguchi K, et al. Development of optical resolution photoacoustic microscopy with sub-micron lateral resolution for visualization of cells and their structures. Jpn J Appl Phys, 2020, 59(SK): 1-11.
12. Bailleul J, Simon B, Debailleul M, et al. Tomographic diffractive microscopy: towards high-resolution 3-D real-time data acquisition, image reconstruction and display of unlabeled samples. Opt Commun, 2018, 422(SI): 28-37.
13. Liu W, Yao J J. Photoacoustic microscopy: principles and biomedical applications. Biomed Eng Lett, 2018, 8(2): 203-213.
14. Schwarz M, Garzorz-Stark N, Eyerich K, et al. Motion correction in optoacoustic mesoscopy. Sci Rep, 2017, 7(1): 10386.
15. Aguirre J, Berezhnoi A, He H L, et al. Motion quantifification and automated correction in clinical RSOM. IEEE Trans Med Imag, 2019, 38(6): 1340-1346.
16. Cheng Z W, Ma H G, Wang Z Y, et al. Subpixel and on-line motion correction for photoacoustic dermoscopy. IEEE J Sel Top Quantum Electron, 2021, 27(4): 6800408.
17. Lan S, Guo Z, You J. Non-rigid medical image registration using image field in demons algorithm. Pattern Recogn Lett, 2019, 125(7): 98-104.
18. Lin L H, Chen T J. Mutual information correlation with human vision in medical image compression. Curr Med Imaging Rev, 2018, 14(1): 64-70.
19. Moothanchery M, Bi R, Kim J Y, et al. Optical resolution photoacoustic microscopy based on multimode fibers. Biomed Opt Express, 2018, 9(3): 1190-1197.
20. 孙正, 王新宇. 深度学习在光声成像中的应用现状. 计算机科学, 2020, 47(6A): 148-152.
21. Chen X X, Qi W Z, Xi L. Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy. Vis Comput Ind Biomed Art, 2019, 2(1): 1-6.
22. 孙正, 闫向阳. 采用稀疏测量数据的有限角度光声层析成像的研究进展. 声学技术, 2020, 39(1): 1-10.
23. 刘强, 金天, 陈倩, 等. 小型化光声成像技术在生物医学领域的研究进展. 中国激光, 2020, 47(2): 256-264.
24. Deán-Ben X L, Bay E. Functional optoacoustic imaging of moving objects using microsecond-delay acquisition of multispectral three-dimensional tomographic data. Sci Rep, 2014, 4: 5878.
25. Märk J, Wagener A. Photoacoustic pump-probe tomography of fluorophores in vivo using interleaved image acquisition for motion suppression. Sci Rep, 2017, 7: 40496.
26. Lu Y H, Fontaine K, Mulnix T, et al. Respiratory motion compensation for PET/CT with motion information derived from matched attenuation-corrected gated PET data. J Nucl Med, 2018, 59(9): 1480-1486.
27. Moghari M H, Barthur A, Amaral M E, et al. Free-breathing whole-heart 3D cine magnetic resonance imaging with prospective respiratory motion compensation. Magn Reson Med, 2018, 80(1): 181-189.
28. Ron A, Davoudi N, Deán-Ben X L, et al. Self-gated respiratory motion rejection for optoacoustic tomography. Appl Sci, 2019, 9(13): 2737.
29. Xia J, Chen W Y, Maslov K, et al. Retrospective respiration-gated whole-body photoacoustic computed tomography of mice. J Biomed Opt, 2014, 19(1): 16003.
30. van de Lindt T, Fast M, Nowee M, et al. PO-0956: A self-gated coronal 4D-MRI method for daily imaging of liver lesions on an MR-Linac. Radiother Oncol, 2018, 127(S5): 23-24.
31. Capo M, Perez A, Lozano J A. A cheap feature selection approach for the k-means algorithm. IEEE T Neur Net Lear, 2020. DOI: 10.1109/TNNLS.2020.3002576.
32. 马健. 基于改进的k-means算法初始化方法研究. 云南民族大学学报(自然科学版), 2020, 29(3): 274-278.
33. Karlas A, Kallmayer M, Fasoula N A, et al. Multispectral optoacoustic tomography of muscle perfusion and oxygenation under arterial and venous occlusion: a human pilot study. J Biophotonics, 2020, 13(6): 1-9.
34. Taruttis A, Claussen J, Razansky D, et al. Motion clustering for deblurring multispectral optoacoustic tomography images of the mouse heart. J Biomed Opt, 2012, 17(1): 016009.
35. Ivankovic I, Merčep E, Schmedt, C G, et al. Real-time volumetric assessment of the human carotid artery: handheld multispectral optoacoustic tomography. Radiology, 2019, 291(1): 45-50.
36. Chung J, Nguyen L. Motion estimation and correction in photoacoustic tomographic reconstruction. SIAM J Imaging Sci, 2017, 10(1): 216-242.
37. Gubinelli M, Koch H, Oh T. Renormalization of the two-dimensional stochastic nonlinear wave equation. T Am Math Soc, 2018, 370(10): 7335-7359.
38. Schuster T, Hahn B, Burger M. Dynamic inverse problems: modelling-regularization-numerics. Inverse Probl, 2018, 34(4): 040301.
39. Chen G Y, Wang S Q, Wang D Q, et al. Regularization methods for separable nonlinear models. Nonlinear Dyn, 2019, 98(2): 1287-1298.
40. Iakymchuk R, Yarmola H, Shakhno S. Gauss-Newton-Secant method for solving nonlinear least squares problems. Proc Appl Math Mech, 2018, 18(1): e201800228.
41. Kim M, Kang J, Chang J H, et al. Image quality improvement based on inter-frame motion compensation for photoacoustic imaging: a preliminary study// Proceedings of 2013 IEEE International Ultrasonics Symposium (IUS). Prague: IEEE, 2013: 1527-1531.
42. Manikandan L C, Nair S A H, Sanalkumar K P, et al. A study and analysis on block matching algorithms for motion estimation in video coding. Cluster Comput, 2019, 22(5): 11773-11780.
43. Miranda C, Marschall E, Browning B, et al. Side-viewing photoacoustic waveguide endoscopy. Photoacoustics, 2020, 19: 100167.
44. Iskander-Rizk S, Wu M, Springeling G, et al. In vivo intravascular photoacoustic imaging of lipid plaque in coronary atherosclerosis. EuroIntervention, 2019, 15(5): 452-456.

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