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We present DeepR\u2010SXYZ, a deep learning framework that achieves pixel dwell time equivalent to hundreds of nanoseconds (0.168\u20130.38 \u00b5s) through sparse X\u2010Y\u2010Z reconstruction for 3PFM. Trained on paired datasets consisting of sparsely acquired low\u2010resolution volumetric scans (e.g., 256 \u00d7 256 pixels, 1.2 \u00b5s per pixel, Z step: 16\u00a0\u00b5m) and their corresponding densely sampled high\u2010resolution counterparts (e.g., 512 \u00d7 512 pixels, 3.2 \u00b5s per pixel, Z step: 2\u00a0\u00b5m), DeepR\u2010SXYZ integrates convolutional neural networks (CNNs) with a structure\u2010dynamic attention (SDA)\u2010enhanced transformer, synergistically capturing intra\u2010layer morphological features (for X\u2010Y plane reconstruction) and inter\u2010layer dynamic variations (for Z\u2010axis interpolation). This design enables accurate 3D volume reconstruction from sparsely sampled data. Experimental validation on cerebral vasculature and muscle macrophages demonstrates that DeepR\u2010SXYZ achieves 8.8\u00d7 acceleration in X\u2010Y plane imaging over conventional dense sampling and &gt;60% Z\u2010axis layer recovery, substantially improving imaging throughput and reducing single\u2010pixel dwell time significantly. Crucially, the framework enables large\u2010field 3D cerebral vasculature imaging and dynamic volumetric tracking of cellular behaviors, revealing previously inaccessible spatiotemporal biological processes. This work establishes a computational paradigm for high\u2010speed, low\u2010phototoxicity three\u2010photon fluorescence microscopic imaging via sparse X\u2010Y\u2010Z reconstruction, effectively balancing imaging speed and spatial resolution.<\/jats:p>","DOI":"10.1002\/smtd.202501513","type":"journal-article","created":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T06:26:11Z","timestamp":1768631171000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hundred\u2010Nanosecond Equivalent Pixel Dwell Time for Deep\u2010Tissue 3D Three\u2010Photon Fluorescence Microscopy via Sparse X\u2010Y\u2010Z Reconstruction"],"prefix":"10.1002","volume":"10","author":[{"given":"Yifei","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Extreme Photonics and Instrumentation Centre For Optical and Electromagnetic Research College of Optical Science and Engineering International Research Center For Advanced Photonics Zhejiang University  Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runnan","family":"Zhang","sequence":"additional","affiliation":[{"name":"The School of Electrical and Information Engineering Tianjin University  Tianjin China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keying","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University\u2010University of Edinburgh Institute Zhejiang University School of Medicine Zhejiang University  Haining China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mubin","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Extreme Photonics and Instrumentation Centre For Optical and Electromagnetic Research College of Optical Science and Engineering International Research Center For Advanced Photonics Zhejiang University  Hangzhou 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