Volume 17 Issue 2
Mar.  2024
Turn off MathJax
Article Contents
CHENG Li-jun, SUN Zheng, SUN Mei-chen, HOU Ying-sa. A photoacoustic tomography image reconstruction method based on forward imaging model[J]. Chinese Optics, 2024, 17(2): 444-455. doi: 10.37188/CO.2023-0114
Citation: CHENG Li-jun, SUN Zheng, SUN Mei-chen, HOU Ying-sa. A photoacoustic tomography image reconstruction method based on forward imaging model[J]. Chinese Optics, 2024, 17(2): 444-455. doi: 10.37188/CO.2023-0114

A photoacoustic tomography image reconstruction method based on forward imaging model

doi: 10.37188/CO.2023-0114
Funds:  Supported by National Natural Science Foundation of China (No. 62071181)
More Information
  • Corresponding author: sunzheng@ncepu.edu.cn
  • Received Date: 22 Jul 2023
  • Rev Recd Date: 24 Aug 2023
  • Available Online: 06 Nov 2023
  • Aiming at the issue of degraded image quality in photoacoustic tomography (PAT) caused by the inhomogeneous light fluence distribution, complex optical and acoustic properties of biological tissues, and non-ideal properties of ultrasonic detectors, we propose a comprehensive forward imaging model. The model takes into account variables such as the inhomogeneous light fluence, unsteady speed of sound, spatial and electrical impulse responses of ultrasonic transducers, limited-view scanning, and sparse sampling. The inverse problem of the imaging model is solved by alternate optimization, and images representing optical absorption and speed of sound (SoS) distributions are reconstructed simultaneously. The results indicate that the structural similarity of the reconstructed images of the proposed method can be enhanced by about 83%, 56%, and 22%, in comparison with back projection, time-reversal, and short-lag spatial coherence techniques, respectively. Additionally, the peak signal-to-noise ratio can be improved by approximately 80%, 68% and 58%, respectively. This method considerably enhances the image quality of non-ideal imaging scenarios when compared to traditional techniques.

     

  • loading
  • [1]
    YAO J J, WANG L V. Recent progress in photoacoustic molecular imaging[J]. Current Opinion in Chemical Biology, 2018, 45: 104-112. doi: 10.1016/j.cbpa.2018.03.016
    [2]
    孙正, 王新宇. 深度学习在光声成像中的应用现状[J]. 计算机科学,2020,47(6A):148-152,156.

    SUN ZH, WANG X Y. Application of deep learning in photoacoustic imaging[J]. Computer Science, 2020, 47(6A): 148-152,156. (in Chinese).
    [3]
    COX B T, LAUFER J G, BEARD P C, et al. Quantitative spectroscopic photoacoustic imaging: a review[J]. Journal of Biomedical Optics, 2012, 17(6): 061202. doi: 10.1117/1.JBO.17.6.061202
    [4]
    JAVAHERIAN A, HOLMAN S. Direct quantitative photoacoustic tomography for realistic acoustic media[J]. Inverse Problems, 2019, 35(8): 084004. doi: 10.1088/1361-6420/ab091e
    [5]
    XU M H, WANG L V. Universal back-projection algorithm for photoacoustic computed tomography[J]. Proceedings of SPIE, 2005, 5697: 251-254. doi: 10.1117/12.589146
    [6]
    SUN ZH, HAN D D, YUAN Y. 2-D image reconstruction of photoacoustic endoscopic imaging based on time-reversal[J]. Computers in Biology and Medicine, 2016, 76: 60-68. doi: 10.1016/j.compbiomed.2016.06.028
    [7]
    SHAN H M, WIEDEMAN C, WANG G, et al. Simultaneous reconstruction of the initial pressure and sound speed in photoacoustic tomography using a deep-learning approach[J]. Proceedings of SPIE, 2019, 11105: 1110504.
    [8]
    LOU Y, WANG K, ORAEVSKY A A, et al. Impact of nonstationary optical illumination on image reconstruction in optoacoustic tomography[J]. Journal of the Optical Society of America A, 2016, 33(12): 2333-2347. doi: 10.1364/JOSAA.33.002333
    [9]
    孟琪, 孙正. 生物光声层析成像中不均匀和不稳定照明解决方法[J]. 中国光学,2021,14(2):307-319. doi: 10.37188/CO.2020-0142

    MENG Q, SUN ZH. Solutions to inhomogeneous and unstable illumination in biological photoacoustic tomography[J]. Chinese Optics, 2021, 14(2): 307-319. (in Chinese). doi: 10.37188/CO.2020-0142
    [10]
    CHO M H, KANG L H, KIM J S, et al. An efficient sound speed estimation method to enhance image resolution in ultrasound imaging[J]. Ultrasonics, 2009, 49(8): 774-778. doi: 10.1016/j.ultras.2009.06.005
    [11]
    NAPOLITANO D, CHOU C H, MCLAUGHLIN G, et al. Sound speed correction in ultrasound imaging[J]. Ultrasonics, 2006, 44 Suppl: e43-e46.
    [12]
    PETROSYAN T, THEODOROU M, BAMBER J, et al. Rapid scanning wide-field clutter elimination in epi-optoacoustic imaging using comb LOVIT[J]. Photoacoustics, 2018, 10: 20-30. doi: 10.1016/j.pacs.2018.02.001
    [13]
    LEDIJU BELL M A, KUO N, SONG D Y, et al. Short-lag spatial coherence beamforming of photoacoustic images for enhanced visualization of prostate brachytherapy seeds[J]. Biomedical Optics Express, 2013, 4(10): 1964-1977. doi: 10.1364/BOE.4.001964
    [14]
    NGUYEN H N Y, HUSSAIN A, STEENBERGEN W. Reflection artifact identification in photoacoustic imaging using multi-wavelength excitation[J]. Biomedical Optics Express, 2018, 9(10): 4613-4630. doi: 10.1364/BOE.9.004613
    [15]
    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. doi: 10.16300/j.cnki.1000-3630.2020.01.001

    SUN ZH, YAN X Y. Progress of limited-view photoacoustic tomography imaging based on sparse measurement[J]. Technical Acoustics, 2020, 39(1): 1-10. (in Chinese). doi: 10.16300/j.cnki.1000-3630.2020.01.001
    [16]
    LI C H, WANG L V. Photoacoustic tomography and sensing in biomedicine[J]. Physics in Medicine & Biology, 2009, 54(19): R59-R97.
    [17]
    HOCHULI R, POWELL S, ARRIDGE S, et al. Forward and adjoint radiance Monte Carlo models for quantitative photoacoustic imaging[J]. Proceedings of SPIE, 2015, 9323: 93231P.
    [18]
    MOHAMMADI L, BEHNAM H, TAVAKKOLI J, et al. Skull’s photoacoustic attenuation and dispersion modeling with deterministic ray-tracing: towards real-time aberration correction[J]. Sensors, 2019, 19(2): 345. doi: 10.3390/s19020345
    [19]
    WANG K, ERMILOV S A, 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. doi: 10.1109/TMI.2010.2072514
    [20]
    LIU D C, NOCEDAL J. On the limited memory BFGS method for large scale optimization[J]. Mathematical Programming, 1989, 45(1-3): 503-528. doi: 10.1007/BF01589116
    [21]
    BECK A, TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183-202. doi: 10.1137/080716542
    [22]
    HIRAKAWA M, NAGAKUBO D, KANZLER B, et al. Fundamental parameters of the developing thymic epithelium in the mouse[J]. Scientific Reports, 2018, 8(1): 11095. doi: 10.1038/s41598-018-29460-0
    [23]
    LU T, WANG Y H, LI J, et al. Full-frequency correction of spatial impulse response in back-projection scheme using space-variant filtering for optoacoustic mesoscopy[J]. Photoacoustics, 2020, 19: 100193. doi: 10.1016/j.pacs.2020.100193
    [24]
    SHENG Q W, WANG K, MATTHEWS T P, 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): 2443-2458. doi: 10.1109/TMI.2015.2437356
    [25]
    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): 114005. doi: 10.1088/1361-6420/ab2a30
    [26]
    LI M L, WANG L V. A study of reconstruction in photoacoustic tomography with a focused transducer[J]. Proceedings of SPIE, 2007, 6437: 64371E.
    [27]
    GAVIN H P. The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems[D]. Durham: Duke University, 2019: 1-19.
    [28]
    王倩, 蔡伟伟, 陶波. 基于层析成像的激光强度分布测量方法[J]. 中国光学(中英文),2023,16(4):743-752.

    WANG Q, CAI W W, TAO B. Laser intensity distribution measurement method based on tomographic imaging[J]. Chinese Optics, 2023, 16(4): 743-752. (in Chinese)
    [29]
    HELOU E S, ZIBETTI M V W, HERMAN G T. Fast proximal gradient methods for nonsmooth convex optimization for tomographic image reconstruction[J]. Sensing and Imaging, 2020, 21(1): 45. doi: 10.1007/s11220-020-00309-z
    [30]
    BECK A, TEBOULLE M. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems[J]. IEEE Transactions on Image Processing, 2009, 18(11): 2419-2434. doi: 10.1109/TIP.2009.2028250
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(12)  / Tables(1)

    Article views(241) PDF downloads(93) Cited by()
    Proportional views

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return