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Updated: Jan 28, 2026

Fluorescence Lifetime Macro Imager for Biomedical Applications
Published on: April 7, 2023
Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing - a deep learning
Ruoyang Yao1, Marien Ochoa1, Pingkun Yan1
1Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.
Abstract:
Macroscopic fluorescence lifetime imaging (MFLI) via compressed sensed (CS) measurements enables efficient and accurate quantification of molecular interactions in vivo over a large field of view (FOV). However, the current data-processing workflow is slow, complex and performs poorly under photon-starved conditions. In this paper, we propose Net-FLICS, a novel image reconstruction method based on a convolutional neural network (CNN), to directly reconstruct the intensity and lifetime images from raw time-resolved CS data. By carefully designing a large simulated dataset, Net-FLICS is successfully trained and achieves outstanding reconstruction performance on both in vitro and in vivo experimental data and even superior results at low photon count levels for lifetime quantification.
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