Spectral and lifetime fluorescence unmixing via deep learning

Abstract

AbstractHyperspectral Fluorescence Lifetime Imaging allows for the simultaneous acquisition of spectrally resolved temporal fluorescence emission decays. In turn, the acquired rich multidimensional data set enables simultaneous imaging of multiple fluorescent species for a comprehensive molecular assessment of biotissues. However, to enable quantitative imaging, inherent spectral overlap between the considered fluorescent probes and potential bleed-through must be taken into account. Such task is performed via either spectral or lifetime unmixing, typically independently. Herein, we present UNMIX-ME (unmix multiple emissions), a deep learning-based fluorescence unmixing routine, capable of quantitative fluorophore unmixing by simultaneously using both spectral and temporal signatures. UNMIX-ME was trained and validated using an in silico framework replicating the data acquisition process of a compressive hyperspectral fluorescent lifetime imaging platform (HMFLI). It was benchmarked against a conventional LSQ method for both tri and quadri-exponential simulated samples. Last, UNMIX-ME’s potential was assessed for NIR FRET in vitro and in vivo for small animal experimental data.