Net-FLICS: fast quantitative wide-field fluorescence lifetime imaging with compressed sensing-a deep learning approach

• 来源期刊: 光：科学与应用（英文版）
• 年，卷（期）: 2019,（2）
• 页码范围: 212-218
• 页数: 7页
• 语种: 英文

摘要

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.