Deep learning enhanced fast fluorescence lifetime imaging with a few photons

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Xiao, Dong and Sapermsap, Natakorn and Chen, Yu and Li, David Day-Uei (2023) Deep learning enhanced fast fluorescence lifetime imaging with a few photons. Other. bioRxiv, Ithaca, New York. (https://doi.org/10.1101/2023.04.06.534322)

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Abstract

We present a deep learning (DL) framework, which we term FPFLI (Few-Photon Fluorescence Lifetime Imaging), for fast analyzing fluorescence lifetime imaging (FLIM) data under highly low-light conditions with only a few photon-per-pixels (PPPs). FPFLI breaks the conventional pixel-wise lifetime analysis paradigm and fully exploits the spatial correlation and intensity information of fluorescence lifetime images to estimate lifetime images, pushing the photon budget to an unprecedented low level. The DL framework can be trained by synthetic FLIM data and easily adapted to various FLIM systems. FPFLI can effectively and robustly estimate FLIM images within seconds using synthetic and experimental data. The fast analysis of low-light FLIM images made possible by FPFLI will promise a broad range of potential applications.

ORCID iDs

Xiao, Dong, Sapermsap, Natakorn, Chen, Yu and Li, David Day-Uei ORCID logoORCID: https://orcid.org/0000-0002-6401-4263;
CORE (COnnecting REpositories)