[1] Drew PJ, Shih AY, Kleinfeld D. Fluctuating and sensory-induced vasodynamics in rodent cortex extend arteriole capacity. P Natl Acad Sci USA. 2011;108:8473–8.
[2] Wang Z, et al. Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning. Nat Methods. 2021;18:551–6.
[3] Andrade-Talavera, Y., Fisahn, A. & Rodríguez-Moreno, A. Timing to be precise? An overview of spike timing-dependent plasticity, brain rhythmicity, and glial cells interplay within neuronal circuits. Mol Psychiatry. 2023;28:2177–88.
[4] Fan JL, et al. High-speed volumetric two-photon fluorescence imaging of neurovascular dynamics. Nat Commun. 2020;11:6020.
[5] Shen Z, Lu Z, Chhatbar PY, O’Herron P, Kara P. An artery-specific fluorescent dye for studying neurovascular coupling. Nat Methods. 2012;9:273–6.
[6] O’Herron P, et al. Neural correlates of single-vessel haemodynamic responses in vivo. Nature. 2016;534:378–82.
[7] Fan J, et al. Video-rate imaging of biological dynamics at centimetre scale and micrometre resolution. Nat Photonics. 2019;13:809–16.
[8] Mickoleit M, et al. High-resolution reconstruction of the beating zebrafish heart. Nat Methods. 2014;11:919–22.
[9] Huang XS, et al. Fast, long-term, super-resolution imaging with Hessian structured illumination microscopy. Nat Biotechnol. 2018;36:451–9.
[10] Zhang, Y.D. et al. A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images. 2019 Ieee/Cvf Conference on Computer Vision and Pattern Recognition (Cvpr 2019), 2019: 11702-11710.
[11] Lee S, Negishi M, Urakubo H, Kasai H, Ishii S. Mu-net: Multi-scale U-net for two-photon microscopy image denoising and restoration. Neural Netw. 2020;125:92–103.
[12] Audier, X., Heuke, S., Volz, P., Rimke, I. & Rigneault, H. Noise in stimulated Raman scattering measurement: From basics to practice. Apl Photonics. 2020;5:011101.
[13] Weigert M, et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat Methods. 2018;15:1090–7.
[14] Lin HN, et al. Microsecond fingerprint stimulated Raman spectroscopic imaging by ultrafast tuning and spatial-spectral learning. Nat Commun. 2021;12:3052.
[15] Shen B, et al. Deep learning autofluorescence-harmonic microscopy. Light. 2022;11:76.
[16] Liu S-B, et al. Deep learning enables parallel camera with enhanced- resolution and computational zoom imaging. PhotoniX. 2023;4:17.
[17] Wang K, et al. Deep learning wavefront sensing and aberration correction in atmospheric turbulence. PhotoniX. 2021;2:8.
[18] Li, Y.P. et al. Fast denoising and lossless spectrum extraction in stimulated Raman scattering microscopy. J Biophotonics. 2021;14:e202100080.
[19] Jiang M, et al. Fiber laser development enabled by machine learning: review and prospect. PhotoniX. 2022;3:16.
[20] Zhao Z, et al. Two-photon synthetic aperture microscopy for minimally invasive fast 3D imaging of native subcellular behaviors in deep tissue. Cell. 2023;186:2475-2491.e2422.
[21] Sarder P, Nehorai A. Deconvolution methods for 3-D fluorescence microscopy images. Ieee Signal Process Mag. 2006;23:32–45.
[22] Figueiredo MAT, Bioucas-Dias JM. Restoration of Poissonian images using alternating direction optimization. Ieee Trans Image Process. 2010;19:3133–45.
[23] Chen JJ, et al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes. Nat Methods. 2021;18:678–87.
[24] Qiao C, et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat Methods. 2021;18:194–202.
[25] Chaudhary, S., Moon, S. & Lu, H. Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning. Nat Commun. 2022;13:5165.
[26] Zhang Y, et al. Multi-focus light-field microscopy for high-speed large-volume imaging. PhotoniX. 2022;3:30.
[27] Junchao F, Xiaoshuai H, Liuju L, Shan T, Liangyi C. A protocol for structured illumination microscopy with minimal reconstruction artifacts. Biophys Rep. 2019;5:80–90.
[28] Xue F, et al. Hessian single-molecule localization microscopy using sCMOS camera. Biophys Rep. 2018;4:215–21.
[29] Lehtinen, J. et al. Noise2Noise: Learning Image Restoration without Clean Data. Int Conference Machine Learn. 2018;80:2965–74.
[30] Lecoq J, et al. Removing independent noise in systems neuroscience data using DeepInterpolation. Nat Methods. 2021;18:1401–8.
[31] Li XY, et al. Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising. Nat Methods. 2021;18:1395–400.
[32] Li X, et al. Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit. Nat Biotechnol. 2023;41:282–92.
[33] Song A, Gauthier JL, Pillow JW, Tank DW, Charles AS. Neural anatomy and optical microscopy (NAOMi) simulation for evaluating calcium imaging methods. J Neurosci Methods. 2021;358:109173.
[34] Zhang Y, et al. Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data. Nat Methods. 2023;20:747–54.
[35] Meiniel W, Olivo-Marin JC, Angelini ED. Denoising of Microscopy Images: A Review of the State-of-the-Art, and a New Sparsity-Based Method. Ieee Trans Image Process. 2018;27:3842–56.
[36] Lorenz KS, Salama P, Dunn KW, Delp EJ. Digital correction of motion artefacts in microscopy image sequences collected from living animals using rigid and nonrigid registration. J Microsc. 2012;245:148–60.
[37] Lee S, Vinegoni C, Sebas M, Weissleder R. Automated motion artifact removal for intravital microscopy, without a priori information. Sci Rep-Uk. 2014;4:4507.
[38] Vinegoni C, Lee S, Aguirre AD, Weissleder R. New techniques for motion-artifact-free in vivo cardiac microscopy. Front Physiol. 2015;6:147.
[39] Pnevmatikakis, Eftychios A, et al. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron. 2016;89:285–99.
[40] Pnevmatikakis EA, Giovannucci A. NoRMCorre: An online algorithm for piecewise rigid motion correction of calcium imaging data. J Neurosci Methods. 2017;291:83–94.
[41] Culley S, et al. Quantitative mapping and minimization of super-resolution optical imaging artifacts. Nat Methods. 2018;15:263–6.
[42] Wang H, et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat Methods. 2019;16:103–10.
[43] Secomb TW. Hemodynamics. Compr Physiol. 2016;6:975–1003.
[44] Peyrounette M, Davit Y, Quintard M, Lorthois S. Multiscale modelling of blood flow in cerebral microcirculation: Details at capillary scale control accuracy at the level of the cortex. Plos One. 2018;13:e0189474.
[45] Munoz CJ, Lucas A, Williams AT, Cabrales P. A review on microvascular hemodynamics. Crit Care Clin. 2020;36:293–305.
[46] Kamoun WS, et al. Simultaneous measurement of RBC velocity, flux, hematocrit and shear rate in vascular networks. Nat Methods. 2010;7:655–60.
[47] Mannam V, et al. Real-time image denoising of mixed Poisson-Gaussian noise in fluorescence microscopy images using ImageJ. Optica. 2022;9:335–45.
[48] Kirillov, A. et al. Segment anything. arXiv:2304.02643. 2023. .
[49] Cheng, Y. et al. Segment and track anything. arXiv:2305.06558. 2023. .
[50] Zhang X, et al. High-resolution mapping of brain vasculature and its impairment in the hippocampus of Alzheimer’s disease mice. Natl Sci Rev. 2019;6:1223–38.
[51] Hove JR, et al. Intracardiac fluid forces are an essential epigenetic factor for embryonic cardiogenesis. Nature. 2003;421:172–7.
[52] Hoage T, Ding Y, Xu X. Quantifying cardiac functions in embryonic and adult zebrafish. Methods Mol Biol (Clifton, NJ). 2012;843:11–20.
[53] De Luca E, et al. ZebraBeat: a flexible platform for the analysis of the cardiac rate in zebrafish embryos. Sci Rep-Uk. 2014;4:4898.
[54] Debarre D, Olivier N, Supatto W, Beaurepaire E. Mitigating Phototoxicity during Multiphoton Microscopy of Live Drosophila Embryos in the 1.0–1.2 μm Wavelength Range. Plos One. 2014;9:e104250.
[55] You S, et al. Intravital imaging by simultaneous label-free autofluorescence-multiharmonic microscopy. Nat Commun. 2018;9:21–5.
[56] Lefort C. A review of biomedical multiphoton microscopy and its laser sources. J Phys D Appl Phys. 2017;50:423001.
[57] Liu H, et al. Visualizing astrocytes in the deep mouse brain in vivo. J Biophotonics. 2019;12:e201800420.
[58] Samanta S, et al. AIE-active two-photon fluorescent nanoprobe with NIR-II light excitability for highly efficient deep brain vasculature imaging. Theranostics. 2021;11:2137–48.
[59] Poon C, Teikari P, Rachmadi MF, Skibbe H, Hynynen K. A dataset of rodent cerebrovasculature from in vivo multiphoton fluorescence microscopy imaging. Sci Data. 2023;10:141.
[60] Qin W, et al. Bright and Photostable Organic Fluorescent Dots with Aggregation-Induced Emission Characteristics for Noninvasive Long-Term Cell Imaging. Adv Funct Mater. 2014;24:635–43.
[61] Wu W, et al. Polymerization-Enhanced Photosensitization. Chem. 2018;4:1937–51.
[62] Florence CM, Baillie LD, Mulligan SJ. Dynamic volume changes in astrocytes are an intrinsic phenomenon mediated by bicarbonate ion flux. Plos One. 2012;7:e51124.
[63] Schindelin J, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9:676–82.
[64] Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T. & Ronneberger, O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. (eds. S. Ourselin, L. Joskowicz, M.R. Sabuncu, G. Unal & W. Wells) 424-432 (Springer International Publishing, Cham; 2016).
[65] Cortinas-Lorenzo B, Perez-Gonzalez F. Adam and the Ants: On the Influence of the Optimization Algorithm on the Detectability of DNN Watermarks. Entropy. 2020;22:1379.
