Projects per year
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 language | English |
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Pages (from-to) | 944-951 |
Number of pages | 8 |
Journal | Optica |
Volume | 10 |
Issue number | 7 |
DOIs | |
Publication status | Published - 18 Jul 2023 |
Keywords
- deep learning
- few-photon fluorescence lifetime imaging (FPFLI)
- network training
- imaging
- low-light conditions
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Dive into the research topics of 'Deep learning enhanced fast fluorescence lifetime imaging with a few photons'. Together they form a unique fingerprint.Projects
- 1 Finished
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SPRINT: A SuPer-Resolution time-resolved ImagiNg and specTroscopy facility for rapid biomolecular analysis
Li, D. (Principal Investigator), Chamberlain, L. (Co-investigator), Chen, Y. (Co-investigator), Cunningham, M. R. (Co-investigator), Gould, G. (Co-investigator), Hoskisson, P. (Co-investigator), McConnell, G. (Co-investigator), Rattray, Z. (Co-investigator) & Van de Linde, S. (Co-investigator)
BBSRC (Biotech & Biological Sciences Research Council)
1/07/21 → 31/05/23
Project: Research