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13 March 2024 Computational fluorescence lifetime imaging
Xavier R. Intes
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Proceedings Volume PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences; PC1285710 (2024) https://doi.org/10.1117/12.3012723
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Fluorescence lifetime imaging (FLI) has become an increasingly popular method as it provides unique insights into numerous biological processes. However, FLI is not a direct imaging modality and datasets need to be postprocessed to quantify fluorescence lifetime or lifetime-based parameters. Such technical implementations can be complex, computationally expensive, require high level of expertise as well as user inputs. Herein, we will report on the development and validation of DL models as fast and user-friendly image formation tools for FLI. To date, our contributions have focused on outputting the quantitative lifetime image from raw FLI measurements without iterative solvers and user input, enabling enhanced multiplexed studies by leveraging both spectral and lifetime contrast simultaneously, performing FLI topography corrected by the tissue optical properties, facilitating the implementation of high-end new instrumental concepts leveraging compressive sensing techniques and performing end-to-end 3D optical reconstructions.
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Xavier R. Intes "Computational fluorescence lifetime imaging", Proc. SPIE PC12857, Computational Optical Imaging and Artificial Intelligence in Biomedical Sciences, PC1285710 (13 March 2024); https://doi.org/10.1117/12.3012723
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KEYWORDS
Fluorescence lifetime imaging
3D image enhancement
3D image reconstruction
Fluorescence imaging
Image acquisition
Image enhancement
Multiplexing
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