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Depth-resolved macroscopic fluorescence lifetime imaging improved though spatial frequency domain imaging

Herein, we report on a depth-resolved Macroscopic Fluorescence Lifetime Imaging (MFLI) analytic framework based around machine learning coupled with a computationally efficient Monte Carlo-based data simulation workflow for robust and user-friendly model training. Our Siamese convolutional neural network (CNN) takes both optical properties (OPs) and time-resolved fluorescence decays as input and reconstructs both lifetime maps and depth profiles simultaneously. We validate our approach using phantom embeddings in silico and experimentally using Spatial Frequency Domain Imaging (SFDI) for OP retrieval. To our knowledge, this is the first study reporting the augmentation of MFLI with wide-field SFDI for lifetime topography.