Deep‐Learning‐Driven High‐Fidelity In Vivo Hyperspectral Fluorescence Imaging Under Extreme Photon‐Limited Conditions

作者：Renjian Li, Shutao Wu, Kaixiang Li, Zhenyu An, Ben Y, Guiye Li, Sunil Kumar, James McGinty, Tawfique Hasan, Songnian Fu, M Q Zhang, Le Chen · 发表于：Advanced Science · 年份：2026 · DOI：10.1002/advs.76802 · 研究领域：Advanced Fluorescence Microscopy Techniques、Optical Imaging and Spectroscopy Techniques、Random lasers and scattering media

In vivo hyperspectral fluorescence imaging (fHSI) has transformed biomedical research by enabling qualitative/quantitative analysis of multiplexed molecular interactions. However, constraints for in vivo imaging and division of photons across numerous spectral channels create extreme photon-limited conditions, where deep signal-to-noise coupling compromises fidelity and prevents accurate analysis. Here, we present a co-designed confocal line-scanning hyperspectral light-sheet microscopy and dual-stream residual attention network with non-negative matrix factorization (DsRAN-NMF), achieving high-fidelity in vivo fHSI with up to three orders-of-magnitude improvement in photon efficiency. Advanced illumination and optical sectioning provide higher-quality initial signals, restored by our DsRAN-NMF with improved spatial and spectral fidelity, which simultaneously comprehends noise physics, high-dimensional data geometry, and hyperspectral unmixing objectives to recover biologically interpretable spectral contributions. This approach resolves highly spectrally overlapping fluorophores at micron-scale resolution in whole live zebrafish and enables visualization of nanoplastic uptake and circulation, establishing a pathway toward 4D hyperspectral imaging of living systems and nanoplastic toxicology.