Hundred-Nanosecond Equivalent Pixel Dwell Time for Deep-Tissue 3D Three-Photon Fluorescence Microscopy via Sparse...

Three-photon fluorescence microscopy (3PFM) enables high-resolution volumetric imaging in deep tissues but is fundamentally constrained by a trade-off among imaging speed and spatial resolution, due to low photon flux and prolonged laser exposure. We present DeepR-SXYZ, a deep learning framework that achieves pixel dwell time equivalent to hundreds of nanoseconds (0.168-0.38 µs) through sparse X-Y-Z reconstruction for 3PFM. Trained on paired datasets consisting of sparsely acquired low-resolution volumetric scans (e.g., 256 × 256 pixels, 1.2 µs per pixel, Z step: 16 µm) and their corresponding densely sampled high-resolution counterparts (e.g., 512 × 512 pixels, 3.2 µs per pixel, Z step: 2 µm), DeepR-SXYZ integrates convolutional neural networks (CNNs) with a structure-dynamic attention (SDA)-enhanced transformer, synergistically capturing intra-layer morphological features (for X-Y plane reconstruction) and inter-layer dynamic variations (for Z-axis interpolation). This design enables accurate 3D volume reconstruction from sparsely sampled data. Experimental validation on cerebral vasculature and muscle macrophages demonstrates that DeepR-SXYZ achieves 8.8× acceleration in X-Y plane imaging over conventional dense sampling and >60% Z-axis layer recovery, substantially improving imaging throughput and reducing single-pixel dwell time significantly. Crucially, the framework enables large-field 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-speed, low-phototoxicity three-photon fluorescence microscopic imaging via sparse X-Y-Z reconstruction, effectively balancing imaging speed and spatial resolution.