Single-pixel hyperspectral macroscopic autofluorescence lifetime imaging and unmixing with deep learning

Marien Ochoa Mendoza; Jason T. Smith; Xavier Intes

doi:10.1117/12.2577898

5 March 2021 Single-pixel hyperspectral macroscopic autofluorescence lifetime imaging and unmixing with deep learning

Marien Ochoa Mendoza, Jason T. Smith, Xavier Intes

Proceedings Volume 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021; 1165505 (2021) https://doi.org/10.1117/12.2577898
Event: SPIE BiOS, 2021, Online Only

Abstract

We propose a Single-Pixel Macroscopic Autofluorescence Imaging platform with supercontinuum excitation (440-690nm) and 16 parallel wavelength detection (475-1000nm) through a spectrophotometer coupled Single Photon Counting PMT. Recorded decays of FAD, POPOP and PPIX commercial auto-fluorophores serve to simulate training samples for UNMIX-ME, a deep learning algorithm that disentangles spectral overlaps. In silico mixed samples are reconstructed as a proof of concept and mixed in vitro samples prepared, measured and reconstructed to unmixed intensity and lifetime images. The results highlight the utility of the platform to macroscopically quantify autofluorescence lifetime in vitro and its future potential for in vivo autofluorescence imaging.

Conference Presentation

Citation Download Citation

Marien Ochoa Mendoza, Jason T. Smith, and Xavier Intes "Single-pixel hyperspectral macroscopic autofluorescence lifetime imaging and unmixing with deep learning", Proc. SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021, 1165505 (5 March 2021); https://doi.org/10.1117/12.2577898

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