Influence of Ultrasonic Detector Characteristics on Image Quality in Biological Photoacoustic Tomography and its Solution
Sun Zheng1,2*, Sun Huifeng1,2
1(Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003,Hebei, China) 2(Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, Hebei, China)
Abstract Biomedical photoacoustic tomography (PAT) is an emerging hybrid functional imaging modality by multi-physics coupling for early detection and accurate diagnosis of tumors and cardiac vessel diseases. For simplicity, most PAT image reconstruction methods are based on an ideal assumption that the photoacoustically generated ultrasonic waves are collected by an ideal point-like detector with an omnidirectional response forming a continuous and complete measuring surface around the object. The influence of the spatial impulse response (SIR) and electrical impulse response (EIR) of the detector on the reconstruction quality is not considered. However, in practical applications, this assumption is usually infeasible, resulting in the reduction in the imaging resolution and the degradation of the image quality. This paper aimed to analyze the influence of the characteristics of the ultrasonic detector on PAT image reconstruction including limited aperture effect, SIR and EIR, directivity, scanning radius, limited view-angle and frequency bandwidth, and positional uncertainty. Moreover, the solutions to above problems were summarized and their advantages, limitations, applications, and potential developments in the future were discussed as well.
Sun Zheng,Sun Huifeng. Influence of Ultrasonic Detector Characteristics on Image Quality in Biological Photoacoustic Tomography and its Solution[J]. Chinese Journal of Biomedical Engineering, 2021, 40(6): 731-742.
[1] Yao Junjie, Wang Lihong. Recent progress in photoacoustic molecular imaging [J]. Current Opinion in Chemical Biology, 2018, 45: 104-112. [2] Guan S, Khan AA, Sikdar S, et al. Limited-view and sparse photoacoustic tomography for neuroimaging with deep learning[J]. Scientific Reports, 2020, 10(1): 8510. [3] Davoudi N, Deán-Ben XL, Razansky D. Deep learning optoacoustic tomography with sparse data [J]. Nature Machine Intelligence, 2019, 1(10): 453-460. [4] Spadin F, Jaeger M, Nuster R, et al. Quantitative comparison of frequency-domain and delay-and-sum optoacoustic image reconstruction including the effect of coherence factor weighting [J]. Photoacoustics, 2020, 17: 100149. [5] Shang Ruibo, Archibald R, Gelb A, et al. Sparsity-based photoacoustic image reconstruction with a linear array transducer and direct measurement of the forward model [J]. Journal of Biomedical Optics, 2019, 24(3): 031015. [6] Wang Bo, Su Tianning, Pang Weiran, et al. Back-projection algorithm in generalized form for circular-scanning-based photoacoustic tomography with improved tangential resolution [J]. Quantitative Imaging in Medicine and Surgery, 2019, 9(3): 491-502. [7] Kumar PP, Naren N, Prabhat M, et al. Inherent error estimates for noisy-data discrimination and filter-specification in universal back-projection based photo-acoustic tomography [J]. Biomedical Physics & Engineering Express, 2018, 4(3): 035022. [8] Kong Qinglin, Gong Rui, Liu Jietao, et al. Investigation on reconstruction for frequency domain photoacoustic imaging via TVAL3 regularization algorithm [J]. IEEE Photonics Journal, 2018, 10(5): 3901215. [9] Bai MR, Chun C, Shih-Syuan L. Iterative algorithm for solving acoustic source characterization problems under block sparsity constraints [J]. The Journal of the Acoustical Society of America, 2018, 143(6): 3747-3757. [10] Poudel J, Lou Yang, Anastasio MA. A survey of computational frameworks for solving the acoustic inverse problem in three-dimensional photoacoustic computed tomography [J]. Physics in Medicine and Biology, 2019, 64: 14TR01. [11] 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[J]. 声学技术, 2020, 39(1): 1-10. [12] Anais VA, Christoph AB, Damien RA, et al. Effects of various generations of iterative CT reconstruction algorithms on low-contrast detectability as a function of the effective abdominal diameter: a quantitative task-based phantom study [J]. Physica Medica, 2018, 48(4): 111-118. [13] Xu Minghua, Wang Lihong. Analytic explanation of spatial resolution related to bandwidth and detector aperture size in themoacoustic or photoacoustic reconstruction [J]. Physical Review E, 2003, 67(5): 1-15. [14] Wang Kun, Ermilov SA, Su R, et al. An imaging model incorporating ultrasonic transducer properties for three-dimensional optoacoustic tomography [J]. IEEE Transactions on Medical Imaging, 2011, 30(2): 203-214. [15] Cox BT and Treeby BE. Effect of sensor directionality on photoacoustic imaging: A study using the k-wave toolbox[C]//Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging and Sensing. San Francisco: SPIE, 2010, 7564: 75640I. [16] Li Menglin and Wang Lihong. A study of reconstruction in photoacoustic tomography with a focused transducer[C]// Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging and Sensing. San Jose: SPIE, 2007, 6437: 64371E. [17] Li Changhui, Ku Geng, Wang Lihong. Improving the image quality of photoacoustic tomography (PAT) by using a negative acoustic lens[C]//Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging and Sensing. San Jose: SPIE, 2008, 6856: 685623. [18] Pramanik M, Ku Geng, Wang Lihong. Tangential resolution improvement in thermoacoustic and photoacoustic tomography using a negative acoustic lens [J]. Journal of Biomedical Optics, 2009,14(2): 024028. [19] Han Jianning, Gui Zhiguo, Wen Tiandun, et al. Direct and real-time sub-wavelength resolution photoacoustic imaging method based on acoustic lens with negative refractive index [J]. Journal of Measurement Science and Instrumentation, 2016, 7(4): 388-397. [20] Li Menglin and Cheng CC. Model-based reconstruction for photoacoustic tomography with finite aperture detectors[C]//Proceedings of IEEE International Ultrasonics Symposium. Rome: IEEE, 2009: 2359-2362. [21] Li ML, Tseng YC, Cheng CC. Model-based correction of finite aperture effect in photo-acoustic tomography [J]. Optics Express, 2010, 18(25): 26285-26292. [22] Li Menglin and Cheng C. Reconstruction of photoacoustic tomography with finite-aperture detectors: deconvolution of the spatial impulse response [J]. Biostatistics, 2010, 7564(17): 75642S. [23] Lingvall F, Olofsson T, Stepinski T. Synthetic aperture imaging using sources with finite aperture: deconvolution of the spatial impulse response [J]. Journal of the Acoustical Society Of America, 2003, 114(1): 225-234. [24] Rabanser S, Neumann L, Haltmeier M, et al. Stochastic proximal gradient algorithms for multi-source quantitative photoacoustic tomography[J]. Entropy, 2018, 20(2): 1-24. [25] Chiu CH, Chuo Y, Li Menglin. Image reconstruction of photoacoustic tomography based on finite-aperture-effect corrected compressed sensing algorithm[C]//Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging & Sensing. San Francisco: SPIE, 2014, 8943: 89433X. [26] Mitsuhashi K, Wang Kun, Anastasio MA, et al. Investigation of the far-field approximation for modeling a transducer's spatial impulse response in photoacoustic computed tomography [J]. Photoacoustics, 2014, 2(1): 21-32. [27] Sheng QW, Wang K, Xia J, et al. Photoacoustic computed tomography without accurate ultrasonic transducer responses[C]//Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging & Sensing. San Francisco: SPIE, 2015, 9323: 932313. [28] Sheng Qiwei, Wang Kun, Matthews TP, et al. A constrained variable projection reconstruction method for photoacoustic computed tomography without accurate knowledge of transducer responses [J]. IEEE Transactions on Medical Imaging, 2015, 34(12): 2443-2457. [29] Chen Guangyou, Gan Min, Chen CL, et al. A regularized variable projection algorithm for separable nonlinear least-squares problems [J]. IEEE Transactions on Automatic Control, 2019, 64(2): 526-537. [30] Drozdov G and Rosenthal A. Analysis of negatively focused ultrasound detectors in optoacoustic tomography [J]. IEEE Transactions on Medical Imaging, 2017, 36(1): 301-309. [31] Drozdov G, Levi A, Rosenthal A, et al. The impulse response of negatively focused spherical ultrasound detectors and its effect on tomographic optoacoustic reconstruction [J]. IEEE Transactions on Medical Imaging, 2019, 38(10): 2326-2337. [32] Rosenthal A, Ntziachristos V, Razansky D. Model-based optoacoustic inversion with arbitrary-shape detectors [J]. Medical Physics, 2011, 38: 4285-4295. [33] Liu Fangyan, Gong Xiaojing, Wang Lihong, et al. Dictionary learning sparse-sampling reconstruction method for in-vivo 3D photoacoustic computed tomography [J]. Biomedical Optics Express, 2019, 10(4): 1660-1677. [34] Deán-Ben XL, Buehler A, Ntziachristos V, et al. Accurate model-based reconstruction algorithm for three-dimensional optoacoustic tomography [J]. IEEE Transactions on Medical Imaging, 2012, 31(10): 1922-1928. [35] Lu Ding, Razansky D, Dean-Ben XL. Model-based reconstruction of large three-dimensional optoacoustic datasets [J]. IEEE Transactions on Medical Imaging, 2020, 39(9): 2931-2940. [36] Caballero MÁA, Gateau J, Déan-Ben XL, et al. Model-based optoacoustic image reconstruction of large three-dimensional tomographic datasets acquired with an array of directional detectors [J]. IEEE Transactions on Medical Imaging, 2014, 33(2): 433-443. [37] Han Y, Ntziachristos V, Rosenthal A. A system analysis and image reconstruction tool for optoacoustic imaging with finite-aperture detectors[C]//Proceedings of SPIE International Conference on Opto-Acoustic Methods and Applications in Biophotonics II. Munich: SPIE, 2015, 9539: 953915. [38] Han Yiyong, Ntziachristos V, Rosenthal A. Optoacoustic image reconstruction and system analysis for finite-aperture detectors under the wavelet-packet framework[J]. Journal of Biomedical Optics, 2016, 21(1): 016002. [39] Lu Ding, Deán-Ben XL, Razansky D. 20 frames per second model-based reconstruction in cross-sectional optoacoustic tomography[C]//Proceedings of SPIE International Conference on Photons Plus Ultrasound: Imaging and Sensing. San Francisco: SPIE, 2017, 10064: 100641A. [40] Lu Ding, Deán-Ben XL, Razansky D. Efficient 3-D model-based reconstruction scheme for arbitrary optoacoustic acquisition geometries [J]. IEEE Transactions on Medical Imaging, 2017, 36(9): 1858-1867. [41] Zangerl G, Moon S, Haltmeier M, et al. Photoacoustic tomography with direction dependent data: an exact series reconstruction approach [J]. Inverse Problems, 2019, 35(11): 1-16. [42] Kalva SK, Hui HH, Pramanik M. Calibrating reconstruction radius in a multi single-element ultrasound-transducer-based photoacoustic computed tomography system [J]. Journal of the Optical Society of America A, 2018, 35(5): 764-771. [43] Burgholzer P, Bauer-Marschallinger J, Grun H. Weight factors for limited angle photoacoustic tomography [J]. Physics in Medicine & Biology, 2009, 54: 3303-3314. [44] Liu Xueyan, Peng Dong, Ma Xibo, et al. Limitedview photoacoustic imaging based on an iterative adaptive weighted filtered backprojection approach [J]. Applied Optics, 2013, 52: 3477-3483. [45] Sun Zheng and Yan Xiangyang. Image reconstruction based on compressed sensing for sparse-data endoscopic photoacoustic tomography [J]. Computers in Biology and Medicine, 2020, 116: 103587. [46] Cao Meng, Feng Ting, Yuan Jie, et al. Spread spectrum photoacoustic tomography with image optimization [J]. IEEE Transactions on Biomedical Circuits System, 2017, 11: 411-419. [47] Rejesh NA, Pullagurla H, Pramanik M. Deconvolution-based deblurring of reconstructed images in photoacoustic/thermoacoustic tomography [J]. Journal of The Optical Society of America A-Optics Image Science and Vision, 2013, 30: 1994-2001. [48] Sun Zheng and Yan Xiangyang. A deep learning method for limited-view intravascular photoacoustic image reconstruction [J]. Journal of Medical Imaging and Health Informatics, 2020, 10(11): 2707-2713. [49] Guan S, Khan AA, Sikdar S, et al. Limited-view and sparse photoacoustic tomography for neuroimaging with deep learning[J]. Scientific Reports, 2020, 10: 8510. [50] Gutta S, Kadimesetty VS, Kalva SK, et al. Deep neural network-based bandwidth enhancement of photoacoustic data [J]. Journal of Biomedical Optics, 2017, 22: 116001. [51] Vu T, Li Mucong, Humayun H, et al. A generative adversarial network for artifact removal in photoacoustic computed tomography with a linear-array transducer [J]. Experimental Biology and Medicine, 2020, 245(7): 597-605. [52] Sahlstrom T, Pulkkinen A, Tick J, et al. Modeling of errors due to uncertainties in ultrasound sensor locations in photoacoustic tomography[J]. IEEE Transactions on Medical Imaging, 2020, 39(6): 2140-2150. [53] Rosenthal A, Ntziachristos V, Razansky D. Optoacoustic methods for frequency calibration of ultrasonic sensors[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 2011, 58(2): 316-326. [54] Antholzer S, Haltmeier M, Schwab J. Deep learning for photoacoustic tomography from sparse data [J]. Inverse Problems in Science and Engineering, 2019, 27(7): 987-1005.
