ArticleLight, science & applications2019
Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing - a deep learning approach.
Article in Light, science & applications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
39 citing papers in PubMed.
- Emerging trends of fluorescence lifetime imaging microscopy (FLIM): advances, challenges, and prospects.Biophysics reports · 2026Article
- High-fidelity fast fluorescence lifetime imaging by event-based denoising.Nature biotechnology · 2026Article
- Fluorescence Lifetime Microscopy Methods for Studying Dynamics of Fluorescent Proteins In Vivo and In Vitro.Advances in experimental medicine and biology · 2026Review
- High-speed wide-field fluorescence lifetime imaging for intraoperative tumor visualization and in vivo multiplexing.Biomedical optics express · 2025Article
- Article
- Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.Sensors (Basel, Switzerland) · 2025Article
- Self-supervised hybrid neural network to achieve quantitative bioluminescence tomography for cancer research.Biomedical optics express · 2024Article
- Deep-LASI, single-molecule data analysis software.Biophysical journal · 2024Article
- 3D convolutional neural networks predict cellular metabolic pathway use from fluorescence lifetime decay data.APL bioengineering · 2024Article
- Applications of machine learning in time-domain fluorescence lifetime imaging: a review.Methods and applications in fluorescence · 2024Review
- Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging.Scientific reports · 2024Article
- Building Fluorescence Lifetime Maps Photon-by-Photon by Leveraging Spatial Correlations.ACS photonics · 2023Article
- Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions.Biomedical optics express · 2023Article
- Fluorescence lifetime: Beating the IRF and interpulse window.Biophysical journal · 2023Article
- Two-Photon Imaging for Non-Invasive Corneal Examination.Sensors (Basel, Switzerland) · 2022Review
- Computational macroscopic lifetime imaging and concentration unmixing of autofluorescence.Journal of biophotonics · 2022Article
- Suppression of natural lens fluorescence in fundus autofluorescence measurements: review of hardware solutions.Biomedical optics express · 2022Review
- Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine.Sensors (Basel, Switzerland) · 2022Article
- Framework for denoising Monte Carlo photon transport simulations using deep learning.Journal of biomedical optics · 2022Article
- Monte Carlo-based data generation for efficient deep learning reconstruction of macroscopic diffuse optical tomography and topography applications.Journal of biomedical optics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.