[1] L. V. Wang, S. Hu. Photoacoustic tomography: in vivo imaging from organelles to organs. Science, 335, 1458-1462(2012).
[2] L. V. Wang, J. Yao. A practical guide to photoacoustic tomography in the life sciences. Nat. Methods, 13, 627-638(2016).
[3] J. Yang, S. Choi, C. Kim. Practical review on photoacoustic computed tomography using curved ultrasound array transducer. Biomed. Eng. Lett., 12, 19-35(2022).
[4] W. Choi et al. Recent advances in contrast-enhanced photoacoustic imaging: overcoming the physical and practical challenges. Chem. Rev., 123, 7379-7419(2023).
[5] W. Choi et al. Three-dimensional multistructural quantitative photoacoustic and US imaging of human feet in vivo. Radiology, 303, 467-473(2022).
[6] S. Lei et al. In vivo three-dimensional multispectral photoacoustic imaging of dual enzyme-driven cyclic cascade reaction for tumor catalytic therapy. Nat. Commun., 13, 1298(2022).
[7] E.-Y. Park et al. Simultaneous dual-modal multispectral photoacoustic and ultrasound macroscopy for three-dimensional whole-body imaging of small animals. Photonics, 8, 13(2021).
[8] N. Kwon et al. Hexa-BODIPY-cyclotriphosphazene based nanoparticle for NIR fluorescence/photoacoustic dual-modal imaging and photothermal cancer therapy. Biosens. Bioelectron., 216, 114612(2022).
[9] C. Kim, C. Favazza, L. V. Wang. In vivo photoacoustic tomography of chemicals: high-resolution functional and molecular optical imaging at new depths. Chem. Rev., 110, 2756-2782(2010).
[10] J. Yang et al. Assessment of nonalcoholic fatty liver function by photoacoustic imaging. J. Biomed. Opt., 28, 016003(2023).
[11] S. Cho et al. 3D PHOVIS: 3D photoacoustic visualization studio. Photoacoustics, 18, 100168(2020).
[12] J. Kim et al. Real-time photoacoustic thermometry combined with clinical ultrasound imaging and high-intensity focused ultrasound. IEEE Trans. Biomed. Eng., 66, 3330-3338(2019).
[13] B. Park et al. Functional photoacoustic imaging: from nano- and micro- to macro-scale. Nano Converg., 10, 29(2023).
[14] J. Li et al. Spatial heterogeneity of oxygenation and haemodynamics in breast cancer resolved in vivo by conical multispectral optoacoustic mesoscopy. Light Sci. Appl., 9, 57(2020).
[15] J. Yang et al. Photoacoustic assessment of hemodynamic changes in foot vessels. J. Biophotonics, 12, e201900004(2019).
[16] J. Yang et al. Detecting hemodynamic changes in the foot vessels of diabetic patients by photoacoustic tomography. J. Biophotonics, 13, e202000011(2020).
[17] J. Yang et al. Photoacoustic imaging of hemodynamic changes in forearm skeletal muscle during cuff occlusion. Biomed. Opt. Express, 11, 4560-4570(2020).
[18] M. R. Tomaszewski et al. Oxygen-enhanced and dynamic contrast-enhanced optoacoustic tomography provide surrogate biomarkers of tumor vascular function, hypoxia, and necrosis. Cancer Res., 78, 5980-5991(2018).
[19] V. M. Sciortino et al. Longitudinal cortex-wide monitoring of cerebral hemodynamics and oxygen metabolism in awake mice using multi-parametric photoacoustic microscopy. J Cereb. Blood Flow Metab., 41, 3187-3199(2021).
[20] J. Yang et al. Intracerebral haemorrhage-induced injury progression assessed by cross-sectional photoacoustic tomography. Biomed. Opt. Express, 8, 5814-5824(2017).
[21] J. Kim et al. Multiparametric photoacoustic analysis of human thyroid cancers in vivo. Cancer Res., 81, 4849-4860(2021).
[22] B. Park et al. 3D wide-field multispectral photoacoustic imaging of human melanomas in vivo: a pilot study. J. Eur. Acad. Dermatol. Venereol., 35, 669-676(2021).
[23] N. Nikhila, X. Jun. Photoacoustic imaging of breast cancer: a mini review of system design and image features. J. Biomed. Opt., 24, 121911(2019).
[24] B. Park, C. Kim, J. Kim. Recent advances in ultrasound and photoacoustic analysis for thyroid cancer diagnosis. Adv. Phys. Res., 2, 2200070(2023).
[25] B. Park et al. Listening to drug delivery and responses via photoacoustic imaging. Adv. Drug Delivery Rev., 184, 114235(2022).
[26] T. Qiu et al. Assessment of liver function reserve by photoacoustic tomography: a feasibility study. Biomed. Opt. Express, 11, 3985-3995(2020).
[27] H. Jung et al. A peptide probe enables photoacoustic-guided imaging and drug delivery to lung tumors in K-rasLA2 mutant mice. Cancer Res., 79, 4271-4282(2019).
[28] S. K. Kalva et al. Rapid volumetric optoacoustic tracking of nanoparticle kinetics across murine organs. ACS Appl. Mater. Interfaces, 14, 172-178(2022).
[29] H. H. Han et al. Bimetallic hyaluronate-modified Au@Pt nanoparticles for noninvasive photoacoustic imaging and photothermal therapy of skin cancer. ACS Appl. Mater. Interfaces, 15, 11609-11620(2023).
[30] T. G. Nguyen Cao et al. Engineered extracellular vesicle-based sonotheranostics for dual stimuli-sensitive drug release and photoacoustic imaging-guided chemo-sonodynamic cancer therapy. Theranostics, 12, 1247-1266(2022).
[31] J. Yao, L. V. Wang. Photoacoustic microscopy. Laser Photonics Rev., 7, 758-778(2013).
[32] J. Ahn et al. Fully integrated photoacoustic microscopy and photoplethysmography of human in vivo. Photoacoustics, 27, 100374(2022).
[33] J. Park et al. Quadruple ultrasound, photoacoustic, optical coherence, and fluorescence fusion imaging with a transparent ultrasound transducer. Proc. Natl. Acad. Sci. U. S. A., 118, e1920879118(2021).
[34] S.-W. Cho et al. High-speed photoacoustic microscopy: a review dedicated on light sources. Photoacoustics, 24, 100291(2021).
[35] J. W. Baik et al. Intraoperative label-free photoacoustic histopathology of clinical specimens. Laser Photonics Rev., 15, 2100124(2021).
[36] J. Ahn et al. High-resolution functional photoacoustic monitoring of vascular dynamics in human fingers. Photoacoustics, 23, 100282(2021).
[37] J. Ahn et al. In vivo photoacoustic monitoring of vasoconstriction induced by acute hyperglycemia. Photoacoustics, 30, 100485(2023).
[38] B. Park et al. Shear-force photoacoustic microscopy: toward super-resolution near-field imaging. Laser Photonics Rev., 16, 2200296(2022).
[39] J. W. Baik et al. Super wide-field photoacoustic microscopy of animals and humans in vivo. IEEE Trans. Med. Imaging, 39, 975-984(2020).
[40] C. Lee et al. Three-dimensional clinical handheld photoacoustic/ultrasound scanner. Photoacoustics, 18, 100173(2020).
[41] C. Lee et al. Panoramic volumetric clinical handheld photoacoustic and ultrasound imaging. Photoacoustics, 31, 100512(2023).
[42] W. Kim et al. Wide-field three-dimensional photoacoustic/ultrasound scanner using a two-dimensional matrix transducer array. Opt. Lett., 48, 343-346(2023).
[43] W. Choi, D. Oh, C. Kim. Practical photoacoustic tomography: realistic limitations and technical solutions. J. Appl. Phys., 127, 230903(2020).
[44] S. K. Kalva, M. Pramanik. Experimental validation of tangential resolution improvement in photoacoustic tomography using modified delay-and-sum reconstruction algorithm. J. Biomed. Opt., 21, 086011(2016).
[45] S. Cho et al. Nonlinear pth root spectral magnitude scaling beamforming for clinical photoacoustic and ultrasound imaging. Opt. Lett., 45, 4575-4578(2020).
[46] S. Jeon et al. Real-time delay-multiply-and-sum beamforming with coherence factor for in vivo clinical photoacoustic imaging of humans. Photoacoustics, 15, 100136(2019).
[47] M. Xu, L. V. Wang. Universal back-projection algorithm for photoacoustic computed tomography. Phys. Rev. E, 71, 016706(2005).
[48] K. P. Köstli, P. C. Beard. Two-dimensional photoacoustic imaging by use of Fourier-transform image reconstruction and a detector with an anisotropic response. Appl. Opt., 42, 1899-1908(2003).
[49] B. E. Treeby, E. Z. Zhang, B. T. Cox. Photoacoustic tomography in absorbing acoustic media using time reversal. Inverse Prob., 26, 115003(2010).
[50] I. Steinberg et al. Superiorized photo-acoustic non-negative reconstruction (spanner) for clinical photoacoustic imaging. IEEE Trans. Med. Imaging, 40, 1888-1897(2021).
[51] S. Bu et al. Model-based reconstruction integrated with fluence compensation for photoacoustic tomography. IEEE Trans. Biomed. Eng., 59, 1354-1363(2012).
[52] S. Choi et al. Deep learning enhances multiparametric dynamic volumetric photoacoustic computed tomography in vivo (DL‐PACT). Adv. Sci. (Weinh.), 10, 2202089(2023).
[53] A. Hariri et al. Deep learning improves contrast in low-fluence photoacoustic imaging. Biomed. Opt. Express, 11, 3360-3373(2020).
[54] J. Li et al. Deep learning-based quantitative optoacoustic tomography of deep tissues in the absence of labeled experimental data. Optica, 9, 32-41(2022).
[55] T. Tong et al. Domain transform network for photoacoustic tomography from limited-view and sparsely sampled data. Photoacoustics, 19, 100190(2020).
[56] S. Jeon et al. A deep learning-based model that reduces speed of sound aberrations for improved in vivo photoacoustic imaging. IEEE Trans. Image Process., 30, 8773-8784(2021).
[57] C. D. Ly et al. Full-view in vivo skin and blood vessels profile segmentation in photoacoustic imaging based on deep learning. Photoacoustics, 25, 100310(2022).
[58] L. Kang et al. Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining. Photoacoustics, 25, 100308(2022).
[59] J. Gröhl et al. Deep learning for biomedical photoacoustic imaging: a review. Photoacoustics, 22, 100241(2021).
[60] X. Zhu et al. Real-time whole-brain imaging of hemodynamics and oxygenation at micro-vessel resolution with ultrafast wide-field photoacoustic microscopy. Light Sci. Appl., 11, 138(2022).
[61] T. C. Benjamin et al. Quantitative spectroscopic photoacoustic imaging: a review. J. Biomed. Opt., 17, 061202(2012).
[62] C. Huang et al. Full-wave iterative image reconstruction in photoacoustic tomography with acoustically inhomogeneous media. IEEE Trans. Med. Imaging, 32, 1097-1110(2013).
[63] F. Y. Wang et al. Where does AlphaGo go: from Church-Turing thesis to AlphaGo thesis and beyond. IEEE/CAA J. Autom. Sin., 3, 113-120(2016).
[64] S. Ioffe, C. Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift, 448-456(2015).
[65] D. E. Rumelhart, G. E. Hinton, R. J. Williams. Learning representations by back-propagating errors. Nature, 323, 533-536(1986).
[66] Y. LeCun et al. Backpropagation applied to handwritten zip code recognition. Neural Comput., 1, 541-551(1989).
[67] S. Ruder. An overview of gradient descent optimization algorithms(2016).
[68] C. Belthangady, L. A. Royer. Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction. Nat. Methods, 16, 1215-1225(2019).
[69] O. Ronneberger, P. Fischer, T. Brox. U-Net: convolutional networks for biomedical image segmentation. Lect. Notes Comput. Sci., 9351, 234-241(2015).
[70] J. Long, E. Shelhamer, T. Darrell. Fully convolutional networks for semantic segmentation, 3431-3440(2015).
[71] I. Goodfellow et al. Generative adversarial networks. Commun. ACM, 63, 139-144(2020).
[72] D. Berthelot, T. Schumm, L. Metz. BEGAN: boundary equilibrium generative adversarial networks(2017).
[73] A. Hauptmann, B. Cox. Deep learning in photoacoustic tomography: current approaches and future directions. J. Biomed. Opt., 25, 112903(2020).
[74] H. Deng et al. Deep learning in photoacoustic imaging: a review. J. Biomed. Opt., 26, 040901(2021).
[75] G. Wissmeyer et al. Looking at sound: optoacoustics with all-optical ultrasound detection. Light Sci. Appl., 7, 53(2018).
[76] G. Sreedevi et al. Deep neural network-based bandwidth enhancement of photoacoustic data. J. Biomed. Opt., 22, 116001(2017).
[77] T. Lu et al. LV-GAN: a deep learning approach for limited-view optoacoustic imaging based on hybrid datasets. J. Biophotonics, 14, e202000325(2021).
[78] H. Lan et al. Y-Net: hybrid deep learning image reconstruction for photoacoustic tomography in vivo. Photoacoustics, 20, 100197(2020).
[79] B. E. Treeby, J. Jaros, B. T. Cox. Advanced photoacoustic image reconstruction using the k-Wave toolbox. Proc. SPIE, 9708, 97082P(2016).
[80] S. Ma, S. Yang, H. Guo. Limited-view photoacoustic imaging based on linear-array detection and filtered mean-backprojection-iterative reconstruction. J. Appl. Phys., 106, 123104(2009).
[81] Y. Tang et al. High-fidelity deep functional photoacoustic tomography enhanced by virtual point sources. Photoacoustics, 29, 100450(2023).
[82] S. Vilov et al. Photoacoustic fluctuation imaging: theory and application to blood flow imaging. Optica, 7, 1495-1505(2020).
[83] H. Deng et al. Machine-learning enhanced photoacoustic computed tomography in a limited view configuration. Proc. SPIE, 11186, 111860J(2019).
[84] K. Simonyan, A. Zisserman. Very deep convolutional networks for large-scale image recognition(2014).
[85] J. Zhang et al. Limited-view photoacoustic imaging reconstruction with dual domain inputs based on mutual information, 1522-1526(2021).
[86] J. Staal et al. Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging, 23, 501-509(2004).
[87] Y. Xu, D. Feng, L. V. Wang. Exact frequency-domain reconstruction for thermoacoustic tomography. I. Planar geometry. IEEE Trans. Med. Imaging, 21, 823-828(2002).
[88] L. Li, L. V. Wang. Recent advances in photoacoustic tomography. BME Front., 2021, 9823268(2021).
[89] S. Guan et al. Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal. IEEE J. Biomed. Health. Inf., 24, 568-576(2019).
[90] P. Farnia et al. High-quality photoacoustic image reconstruction based on deep convolutional neural network: towards intra-operative photoacoustic imaging. Biomed. Phys. Eng. Express, 6, 045019(2020).
[91] M. Guo et al. AS-Net: fast photoacoustic reconstruction with multi-feature fusion from sparse data. IEEE Trans. Comput. Imaging, 8, 215-223(2022).
[92] H. Lan et al. Ki-GAN: knowledge infusion generative adversarial network for photoacoustic image reconstruction in vivo. Lect. Notes Comput. Sci., 11764, 273-281(2019).
[93] A. DiSpirito et al. Reconstructing undersampled photoacoustic microscopy images using deep learning. IEEE Trans. Med. Imaging, 40, 562-570(2020).
[94] M. Chen et al. Simultaneous photoacoustic imaging of intravascular and tissue oxygenation. Opt. Lett., 44, 3773-3776(2019).
[95] T. Vu et al. Deep image prior for undersampling high-speed photoacoustic microscopy. Photoacoustics, 22, 100266(2021).
[96] G. Godefroy, B. Arnal, E. Bossy. Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties. Photoacoustics, 21, 100218(2021).
[97] Y. Gal, Z. Ghahramani. Dropout as a Bayesian approximation: representing model uncertainty in deep learning, 1050-1059(2016).
[98] T. Vu et al. A generative adversarial network for artifact removal in photoacoustic computed tomography with a linear-array transducer. Exp. Biol. Med. (Maywood), 245, 597-605(2020).
[99] C. Ledig et al. Photo-realistic single image super-resolution using a generative adversarial network, 4681-4690(2017).
[100] H. Zhang et al. A new deep learning network for mitigating limited-view and under-sampling artifacts in ring-shaped photoacoustic tomography. Comput. Med. Imaging Graph., 84, 101720(2020).
[101] N. Davoudi, X. L. Deán-Ben, D. Razansky. Deep learning optoacoustic tomography with sparse data. Nat. Mach. Intell., 1, 453-460(2019).
[102] N. Davoudi et al. Deep learning of image-and time-domain data enhances the visibility of structures in optoacoustic tomography. Opt. Lett., 46, 3029-3032(2021).
[103] N. Awasthi et al. Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography. IEEE Trans. Ultrasonics, Ferroelectr. Freq. Control, 67, 2660-2673(2020).
[104] D.-A. Clevert, T. Unterthiner, S. Hochreiter. Fast and accurate deep network learning by exponential linear units (ELUS)(2015).
[105] J. Schwab et al. Real-time photoacoustic projection imaging using deep learning(2018).
[106] T. Karras et al. Progressive growing of GANS for improved quality, stability, and variation(2017).
[107] K. Daoudi et al. Handheld probe integrating laser diode and ultrasound transducer array for ultrasound/photoacoustic dual modality imaging. Opt. Express, 22, 26365-26374(2014).
[108] A. Hariri et al. The characterization of an economic and portable LED-based photoacoustic imaging system to facilitate molecular imaging. Photoacoustics, 9, 10-20(2018).
[109] P. Rajendran, M. Pramanik. High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning. J. Biomed. Opt., 27, 066005(2022). https://doi.org/10.1117/1.JBO.27.6.066005
[110] H. Zhao et al. Deep learning enables superior photoacoustic imaging at ultralow laser dosages. Adv. Sci. (Weinh.), 8, 2003097(2021).
[111] M. K. A. Singh et al. Deep learning-enhanced LED-based photoacoustic imaging. Proc. SPIE, 11240, 1124038(2020).
[112] E. M. A. Anas et al. Towards a fast and safe LED-based photoacoustic imaging using deep convolutional neural network. Lect. Notes Comput. Sci., 11073, 159-167(2018).
[113] L. R. Medsker, L. Jain. Recurrent Neural Networks(2001).
[114] E. M. A. Anas et al. Enabling fast and high quality LED photoacoustic imaging: a recurrent neural networks based approach. Biomed. Opt. Express, 9, 3852-3866(2018).
[115] M. Li, Y. Tang, J. Yao. Photoacoustic tomography of blood oxygenation: a mini review. Photoacoustics, 10, 65-73(2018).
[116] S. Tzoumas et al. Eigenspectra optoacoustic tomography achieves quantitative blood oxygenation imaging deep in tissues. Nat. Commun., 7, 1-10(2016).
[117] A. Rosenthal, D. Razansky, V. Ntziachristos. Fast semi-analytical model-based acoustic inversion for quantitative optoacoustic tomography. IEEE Trans. Med. Imaging, 29, 1275-1285(2010).
[118] X. L. Deán-Ben, D. Razansky. A practical guide for model-based reconstruction in optoacoustic imaging. Front. Phys., 10, 1057(2022).
[119] C. Cai et al. End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging. Opt. Lett., 43, 2752-2755(2018).
[120] C. Yang et al. Quantitative photoacoustic blood oxygenation imaging using deep residual and recurrent neural network, 741-744(2019).
[121] G. P. Luke et al. O-Net: a convolutional neural network for quantitative photoacoustic image segmentation and oximetry(2019).
[122] C. Yang, F. Gao. EDA-Net: dense aggregation of deep and shallow information achieves quantitative photoacoustic blood oxygenation imaging deep in human breast. Lect. Notes Comput. Sci., 11764, 246-254(2019).
[123] J. Gröhl et al. Estimation of blood oxygenation with learned spectral decoloring for quantitative photoacoustic imaging (LSD-qPAI)(2019).
[124] C. Bench, A. Hauptmann, B. Cox. Toward accurate quantitative photoacoustic imaging: learning vascular blood oxygen saturation in three dimensions. J. Biomed. Opt., 25, 085003(2020).
[125] Y. Zou et al. Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions. Photoacoustics, 28, 100420(2022).
[126] T. Chen et al. A deep learning method based on U-Net for quantitative photoacoustic imaging. Proc. SPIE, 11240, 112403V(2020).
[127] J. Gröhl et al. Confidence estimation for machine learning-based quantitative photoacoustics. J. Imaging, 4, 147(2018).
[128] Y. Wang et al. Nonlinear iterative perturbation scheme with simplified spherical harmonics (SP3) light propagation model for quantitative photoacoustic tomography. J. Biophotonics, 14, e202000446(2021).
[129] G.-S. Jeng et al. Real-time interleaved spectroscopic photoacoustic and ultrasound (PAUS) scanning with simultaneous fluence compensation and motion correction. Nat. Commun., 12, 716(2021).
[130] S. Park et al. Normalization of optical fluence distribution for three-dimensional functional optoacoustic tomography of the breast. J. Biomed. Opt., 27, 036001(2022).
[131] J. Zhu et al. Self-fluence-compensated functional photoacoustic microscopy. IEEE Trans. Med. Imaging, 40, 3856-3866(2021).
[132] A. Madasamy et al. Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging. J. Biomed. Opt., 27, 106004(2022).
[133] Z. Zhang, Q. Liu, Y. Wang. Road extraction by deep residual U-Net. IEEE Geosci. Remote Sens. Lett., 15, 749-753(2018).
[134] A. Creswell et al. Generative adversarial networks: an overview. IEEE Signal Process Mag., 35, 53-65(2018).
[135] D. A. Durairaj et al. Unsupervised deep learning approach for photoacoustic spectral unmixing. Proc. SPIE, 11240, 112403H(2020).
[136] I. Olefir et al. Deep learning-based spectral unmixing for optoacoustic imaging of tissue oxygen saturation. IEEE Trans. Med. Imaging, 39, 3643-3654(2020).
[137] S. Guan et al. Limited-view and sparse photoacoustic tomography for neuroimaging with deep learning. Sci. Rep., 10, 8510(2020).
[138] J. Feng et al. End-to-end Res-Unet based reconstruction algorithm for photoacoustic imaging. Biomed. Opt. Express, 11, 5321-5340(2020).
[139] W. Dominik et al. Reconstruction of initial pressure from limited view photoacoustic images using deep learning. Proc. SPIE, 10494, 104942S(2018).
[140] A. Stephan et al. Photoacoustic image reconstruction via deep learning. Proc. SPIE, 10494, 104944U(2018).
[141] H. Lan et al. Reconstruct the photoacoustic image based on deep learning with multi-frequency ring-shape transducer array, 7115-7118(2019).
[142] C. Yang, H. Lan, F. Gao. Accelerated photoacoustic tomography reconstruction via recurrent inference machines, 6371-6374(2019).
[143] M. Kim et al. Deep-learning image reconstruction for real-time photoacoustic system. IEEE Trans. Med. Imaging, 39, 3379-3390(2020).
[144] A. Hauptmann et al. Model-based learning for accelerated, limited-view 3-D photoacoustic tomography. IEEE Trans. Med. Imaging, 37, 1382-1393(2018).
[145] A. Hauptmann et al. Approximate k-space models and deep learning for fast photoacoustic reconstruction. Lect. Notes Comput. Sci., 11074, 103-111(2018).
[146] M. K. A. Singh, W. Steenbergen. Photoacoustic-guided focused ultrasound (PAFUSion) for identifying reflection artifacts in photoacoustic imaging. Photoacoustics, 3, 123-131(2015).
[147] R. Austin, A. L. B. Muyinatu. A machine learning approach to identifying point source locations in photoacoustic data. Proc. SPIE, 10064, 100643J(2017).
[148] D. Allman, A. Reiter, M. A. L. Bell. A machine learning method to identify and remove reflection artifacts in photoacoustic channel data, 1-4(2017).
[149] S. Ren et al. Faster R-CNN: towards real-time object detection with region proposal networks(2015).
[150] H. Shan, G. Wang, Y. Yang. Accelerated correction of reflection artifacts by deep neural networks in photo-acoustic tomography. Appl. Sci., 9, 2615(2019).
[151] P. Stefanov, Y. Yang. Multiwave tomography in a closed domain: averaged sharp time reversal. Inverse Prob., 31, 065007(2015).
[152] Z. Belhachmi, T. Glatz, O. Scherzer. A direct method for photoacoustic tomography with inhomogeneous sound speed. Inverse Prob., 32, 045005(2016).
[153] V. Badrinarayanan, A. Kendall, R. Cipolla. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 39, 2481-2495(2017).
[154] N.-K. Chlis et al. A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography. Photoacoustics, 20, 100203(2020).
[155] X. Lin et al. Variable speed of sound compensation in the linear-array photoacoustic tomography using a multi-stencils fast marching method. Biomed. Signal Process. Control, 44, 67-74(2018).
[156] B. Treeby et al. Automatic sound speed selection in photoacoustic image reconstruction using an autofocus approach. J. Biomed. Opt., 16, 090501(2011).
[157] D. Allman, A. Reiter, M. A. L. Bell. Photoacoustic source detection and reflection artifact removal enabled by deep learning. IEEE Trans. Med. Imaging, 37, 1464-1477(2018).
[158] E. Moen et al. Deep learning for cellular image analysis. Nat. Methods, 16, 1233-1246(2019).
[159] S. Misra et al. Deep learning-based multimodal fusion network for segmentation and classification of breast cancers using B-mode and elastography ultrasound images. Bioeng. Transl. Med., e10480(2022).
[160] J. Zhang et al. Photoacoustic image classification and segmentation of breast cancer: a feasibility study. IEEE Access, 7, 5457-5466(2019).
[161] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet classification with deep convolutional neural networks. Commun. ACM, 60, 84-90(2017).
[162] C. Szegedy et al. Going deeper with convolutions, 1-9(2015).
[163] K. Jnawali et al. Transfer learning for automatic cancer tissue detection using multispectral photoacoustic imaging. Proc. SPIE, 10950, 109503W(2019).
[164] C. Szegedy et al. Inception-v4, inception-ResNet and the impact of residual connections on learning. Proc. AAAI Conf. Artif. Intell., 31, 4278-4284(2017).
[165] K. Jnawali et al. Deep 3D convolutional neural network for automatic cancer tissue detection using multispectral photoacoustic imaging. Proc. SPIE, 10955, 109551D(2019).
[166] S. Moustakidis et al. Fully automated identification of skin morphology in raster-scan optoacoustic mesoscopy using artificial intelligence. Med. Phys., 46, 4046-4056(2019).
[167] W. A. Belson. Matching and prediction on the principle of biological classification. J. R. Stat. Soc.: Ser. C (Appl. Stat.), 8, 65-75(1959).
[168] C. Cortes, V. Vapnik. Support-vector networks. Mach. Learn., 20, 273-297(1995).
[169] S. Nitkunanantharajah et al. Three-dimensional optoacoustic imaging of nailfold capillaries in systemic sclerosis and its potential for disease differentiation using deep learning. Sci. Rep., 10, 16444(2020).
[170] M. Schellenberg et al. Semantic segmentation of multispectral photoacoustic images using deep learning. Photoacoustics, 26, 100341(2022).
[171] B. Lafci et al. Deep learning for automatic segmentation of hybrid optoacoustic ultrasound (OPUS) images. IEEE Trans. Ultrasonics, Ferroelectr. Freq. Control, 68, 688-696(2021).
[172] Y. E. Boink, S. Manohar, C. Brune. A partially-learned algorithm for joint photo-acoustic reconstruction and segmentation. IEEE Trans. Med. Imaging, 39, 129-139(2020).
[173] M. Schwarz et al. Motion correction in optoacoustic mesoscopy. Sci. Rep., 7, 10386(2017).
[174] X. Tong et al. Non-invasive 3D photoacoustic tomography of angiographic anatomy and hemodynamics of fatty livers in rats. Adv. Sci. (Weinh.), 10, 2205759(2023).
[175] X. Chen, W. Qi, L. Xi. Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy. Vis. Comput. Ind. Biomed. Art, 2, 12(2019).
[176] S. Zheng et al. A deep learning method for motion artifact correction in intravascular photoacoustic image sequence. IEEE Trans. Med. Imaging, 42, 66-78(2023).
[177] M. Jaderberg, K. Simonyan, A. Zisserman. Spatial transformer networks(2015).
[178] S. Cheng et al. High-resolution photoacoustic microscopy with deep penetration through learning. Photoacoustics, 25, 100314(2022).
[179] I. Gulrajani et al. Improved training of Wasserstein GANS(2017).
[180] J. Kim et al. Deep learning acceleration of multiscale superresolution localization photoacoustic imaging. Light Sci. Appl., 11, 131(2022).
[181] Z. Zhang et al. Deep and domain transfer learning aided photoacoustic microscopy: acoustic resolution to optical resolution. IEEE Trans. Med. Imaging, 41, 3636-3648(2022).
[182] C. Dehner et al. Deep-learning-based electrical noise removal enables high spectral optoacoustic contrast in deep tissue. IEEE Trans. Med. Imaging, 41, 3182-3193(2022).
[183] D. He et al. De-noising of photoacoustic microscopy images by attentive generative adversarial network. IEEE Trans. Med. Imaging, 42, 1349-1362(2022).
[184] O. Gulenko et al. Deep-learning-based algorithm for the removal of electromagnetic interference noise in photoacoustic endoscopic image processing. Sensors, 22, 3961(2022).
[185] J. Kim et al. Deep learning alignment of bidirectional raster scanning in high speed photoacoustic microscopy. Sci. Rep., 12, 16238(2022).
[186] J. Kim et al. Super-resolution localization photoacoustic microscopy using intrinsic red blood cells as contrast absorbers. Light Sci. Appl., 8, 103(2019).
[187] W. Choi, C. Kim. Toward in vivo translation of super-resolution localization photoacoustic computed tomography using liquid-state dyed droplets. Light Sci. Appl., 8, 57(2019).
[188] P. Isola et al. Image-to-image translation with conditional adversarial networks, 1125-1134(2017).
[189] T. T. W. Wong et al. Fast label-free multilayered histology-like imaging of human breast cancer by photoacoustic microscopy. Sci. Adv., 3, e1602168(2017).
[190] B. Bai et al. Deep learning-enabled virtual histological staining of biological samples. Light Sci. Appl., 12, 57(2023).
[191] M. Boktor et al. Virtual histological staining of label-free total absorption photoacoustic remote sensing (TA-PARS). Sci. Rep., 12, 10296(2022).
[192] R. Cao et al. Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy. Nat. Biomed. Eng., 7, 124-134(2023).
[193] J.-Y. Zhu et al. Unpaired image-to-image translation using cycle-consistent adversarial networks, 2223-2232(2017).
[194] L. Yu et al. Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms. J. Biomed. Opt., 23, 010504(2018).
[195] S. Bohndiek. Addressing photoacoustics standards. Nat. Photonics, 13, 298-298(2019).
[196] L. Qi et al. Photoacoustic tomography image restoration with measured spatially variant point spread functions. IEEE Trans. Med. Imaging, 40, 2318-2328(2021).
[197] R. Shnaiderman et al. A submicrometre silicon-on-insulator resonator for ultrasound detection. Nature, 585, 372-378(2020).
[198] E. Merčep et al. Transmission–reflection optoacoustic ultrasound (TROPUS) computed tomography of small animals. Light Sci. Appl., 8, 18(2019).
[199] G. Kim et al. Integrated deep learning framework for accelerated optical coherence tomography angiography. Sci. Rep., 12, 1289(2022).
[200] S. Misra et al. Bi-modal transfer learning for classifying breast cancers via combined B-mode and ultrasound strain imaging. IEEE Trans. Ultrasonics, Ferroelectr. Freq. Control, 69, 222-232(2022).
[201] S. Misra et al. Multi-channel transfer learning of chest x-ray images for screening of COVID-19. Electronics, 9, 1388(2020).
[202] C. Yoon et al. Collaborative multi-modal deep learning and radiomic features for classification of strokes within 6 h. Expert Syst. Appl., 228, 120473(2023).
[203] S. Misra et al. A voting-based ensemble feature network for semiconductor wafer defect classification. Sci. Rep., 12, 16254(2022).
[204] S. Kim et al. Convolutional neural network–based metal and streak artifacts reduction in dental CT images with sparse-view sampling scheme. Med. Phys., 49, 6253-6277(2022).
[205] S. Choi et al. In situ x-ray-induced acoustic computed tomography with a contrast agent: a proof of concept. Opt. Lett., 47, 90-93(2022).
[206] S. Choi et al. Synchrotron x-ray induced acoustic imaging. Sci. Rep., 11, 4047(2021).
[207] H. Kim et al. Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci. Rep., 11, 22520(2021).