Fluorescence lifetime imaging (FLI) has become an increasingly popular method in molecular imaging as it provides unique insights into the biological processes. However, despite its popularity and profound impact, 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, including outputting the quantitative lifetime image from raw FLI measurements without iterative solvers and user input, performing FLI topography corrected by the tissue optical properties and performing end-to-end 3D optical reconstructions.
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