Deep learning enhanced fast fluorescence lifetime imaging with a few photons

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. Optica, 10 (7). pp. 944-951. ISSN 1899-7015 (https://doi.org/10.1364/OPTICA.491798)

[thumbnail of Xiao-etal-Optica-2023-Deep-learning-enhanced-fast-fluorescence-lifetime-imaging-with-a-few-photons]
Preview
 Text. Filename: Xiao_etal_Optica_2023_Deep_learning_enhanced_fast_fluorescence_lifetime_imaging_with_a_few_photons.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (10MB)| Preview

Abstract

We present a deep learning (DL) framework, termed few-photon fluorescence lifetime imaging (FPFLI), for fast analysis of fluorescence lifetime imaging (FLIM) data under highly low-light conditions with only a few photons per pixel. 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 promises a broad range of potential applications.

CORE (COnnecting REpositories)