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 ...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 dataprocessing 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.展开更多
基金supported by the National Institutes of Health Grants R01 EB19443 and R01 CA207725.
文摘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 dataprocessing 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.
