[1] Wang L V. Multiscale photoacoustic microscopy and computed tomography[J]. Nature Photonics, 3, 503-509(2009).

[2] Luke G P, Emelianov S Y. Label-free detection of lymph node metastases with US-guided functional photoacoustic imaging[J]. Radiology, 277, 435-442(2015).

[3] Tian C, Qian W, Shao X et al. Plasmonic nanoparticles with quantitatively controlled bioconjugation for photoacoustic imaging of live cancer cells[J]. Advanced Science, 3, 1600237(2016).

[4] Tian C, Xie Z X, Fabiilli M L et al. Imaging and sensing based on dual-pulse nonlinear photoacoustic contrast: a preliminary study on fatty liver[J]. Optics Letters, 40, 2253-2256(2015).

[5] Liu S D, Wang H, Zhang C X et al. In vivo photoacoustic sentinel lymph node imaging using clinically-approved carbon nanoparticles[J]. IEEE Transactions on Biomedical Engineering, 67, 2033-2042(2020).

[6] Wang H, Liu S D, Wang T et al. Three-dimensional interventional photoacoustic imaging for biopsy needle guidance with a linear array transducer[J]. Journal of Biophotonics, 12, e201900212(2019).

[7] Tian C, Zhang C X, Zhang H R et al. Spatial resolution in photoacoustic computed tomography[J]. Reports on Progress in Physics, 84, 036701(2021).

[8] Tian C, Pei M L, Shen K et al. Impact of system factors on the performance of photoacoustic tomography scanners[J]. Physical Review Applied, 13, 014001(2020).

[9] Xu M H, Wang L V. Universal back-projection algorithm for photoacoustic computed tomography[J]. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 71, 016706(2005).

[10] Wang K, Su R, Oraevsky A A et al. Investigation of iterative image reconstruction in three-dimensional optoacoustic tomography[J]. Physics in Medicine and Biology, 57, 5399-5423(2012).

[11] Calvetti D, Morigi S, Reichel L et al. Tikhonov regularization and the L-curve for large discrete ill-posed problems[J]. Journal of Computational and Applied Mathematics, 123, 423-446(2000).

[12] Shaw C B, Prakash J, Pramanik M et al. Least squares QR-based decomposition provides an efficient way of computing optimal regularization parameter in photoacoustic tomography[J]. Journal of Biomedical Optics, 18, 080501(2013).

[13] Wang J, Wang Y Y. Photoacoustic imaging reconstruction using combined nonlocal patch and total-variation regularization for straight-line scanning[J]. Biomedical Engineering Online, 17, 105(2018).

[14] Zhang Y, Wang Y Y, Zhang C. Total variation based gradient descent algorithm for sparse-view photoacoustic image reconstruction[J]. Ultrasonics, 52, 1046-1055(2012).

[15] Li X P, Qi L, Zhang S Y et al. Model-based optoacoustic tomography image reconstruction with non-local and sparsity regularizations[J]. IEEE Access, 7, 102136-102148(2019).

[16] Chen S S, Chen M H, Ma W F. Research on Automatic Classification of Optical CoherenceTomography Retina Image Based on Multi-Channe[J]. Chinese Journal of Lasers, 48, 2307001(2021).

[17] Wang C, Qin H T, Lai G Y et al. Automated classification of dual channel dental imaging of auto-fluorescence and white lightby convolutional neural networks[J]. Journal of Innovative Optical Health Sciences, 13, 2050014(2020).

[18] He C E, Xu H J, Wang Z et al. Automatic Segmentation Algorithm for Multimodal Magnetic Resonance-Based Brain Tumor Images[J]. Acta Optica Sinica, 40, 0610001(2020).

[19] Zheng G, Jiang Y F, Shi C et al. Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images[J]. Journal of Innovative Optical Health Sciences, 14, 2140002(2021).

[20] Zhang H L, Li Q, Guan X. An improved three-dimensional dual-path brain tumor image segmentation network[J]. Acta Optica Sinica, 41, 0310002(2021).

[21] Zhu S Q, Wang J, Cai Y F. Low-Dose CT Denoising Algorithm Based on Improved Cycle GAN[J]. Acta Optica Sinica, 40, 2210002(2020).

[22] Awasthi N, Jain G, Kalva S K et al. Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography[J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67, 2660-2673(2020).

[23] Zhang J Y, Chen B, Zhou M et al. Photoacoustic image classification and segmentation of breast cancer: a feasibility study[J]. IEEE Access, 7, 5457-5466(2018).

[24] Tong T, Huang W H, Wang K et al. Domain transform network for photoacoustic tomography from limited-view and sparsely sampled data[J]. Photoacoustics, 19, 100190(2020).

[25] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

[26] Davoudi N, Deán-Ben X L, Razansky D. Deep learning optoacoustic tomography with sparse data[J]. Nature Machine Intelligence, 1, 453-460(2019).

[27] Deng H D, Wang X H, Cai C J et al. Machine-learning enhanced photoacoustic computed tomography in a limited view configuration[J]. Proceedings of SPIE, 11186, 111860J(2019).

[28] Lan H R, Yang C C, Jiang D H et al. Deep learning approach to reconstruct the photoacoustic image using multi-frequency data[C], 487-489(2019).

[29] Shahid H, Khalid A, Liu X et al. A deep learning approach for the photoacoustic tomography recovery from undersampled measurements[J]. Frontiers in Neuroscience, 15, 598693(2021).

[30] Feng J C, Deng J G, Li Z et al. End-to-end Res-Unet based reconstruction algorithm for photoacoustic imaging[J]. Biomedical Optics Express, 11, 5321-5340(2020).

[31] Guan S, Khan A A, Sikdar S et al. Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal[J]. IEEE Journal of Biomedical and Health Informatics, 24, 568-576(2020).

[32] Guan S, Khan A A, Sikdar S et al. Limited-view and sparse photoacoustic tomography for neuroimaging with deep learning[J]. Scientific Reports, 10, 8510(2020).

[33] Liu P J, Zhang H Z, Lian W et al. Multi-level wavelet convolutional neural networks[J]. IEEE Access, 7, 74973-74985(2019).

[34] Hariri A, Alipour K, Mantri Y et al. Deep learning improves contrast in low-fluence photoacoustic imaging[J]. Biomedical Optics Express, 11, 3360-3373(2020).

[35] Aster R C, Borchers B, Thurber C H[M]. Parameter estimation and inverse problems, 165-170(2019).

[36] Zhang L, Zhang L, Mou X Q et al. A comprehensive evaluation of full reference image quality assessment algorithms[C], 1477-1480(2012).

[37] Wang Z, Bovik A C, Sheikh H R et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 13, 600-612(2004).

[38] Wang Z, Simoncelli E P, Bovik A C. Multiscale structural similarity for image quality assessment[C], 1398-1402(2003).

[39] Zhao H, Gallo O, Frosio I et al. Loss functions for image restoration with neural networks[J]. IEEE Transactions on Computational Imaging, 3, 47-57(2017).

[40] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. https://arxiv.org/abs/1412.6980

[41] Hoover A, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response[J]. Proceedings. AMIA Symposium, 931-935(1998).