Deep learning enhanced fast fluorescence lifetime imaging with a few photons

Dong Xiao, Natakorn Sapermsap, Yu Chen, David Day Uei Li

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
55 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)944-951
Number of pages8
JournalOptica
Volume10
Issue number7
DOIs
Publication statusPublished - 18 Jul 2023

Keywords

  • deep learning
  • few-photon fluorescence lifetime imaging (FPFLI)
  • network training
  • imaging
  • low-light conditions

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