Computational imaging study demonstrates robust fluorescence microscopy through scattering biological tissues, suggesting expanded depth and clarity without complex instrumentation.
Biological tissues, as natural scattering media, inherently disrupt structural information, presenting significant challenges for optical imaging. Complex light propagation through tissue severely degrades image quality, limiting conventional fluorescence imaging techniques to superficial depths. Extracting meaningful information from random speckle patterns is, therefore, critical for deeper tissue imaging. In this study, we present RNP (robust non-negative principal matrix factorization), an approach that enables fluorescence microscopy under diverse scattering conditions. By integrating robust feature extraction with non-negativity constraints, RNP effectively addresses challenges posed by non-sparse signals and background interference in scattering tissue environments. The framework operates on a standard epi-fluorescence platform, eliminating the need for complex instrumentation or precise alignment. The results from imaging scattered cells and tissues demonstrate substantial improvements in robustness, field of view, depth of field, and image clarity. We anticipate that RNP will become a valuable tool for overcoming scattering challenges in fluorescence microscopy and driving advancements in biomedical research.
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Gao et al. (2025) studied this question.
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