Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging.

ABSTRACT: Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, and robustness. Inspired by the concept of Edge Artificial Intelligence (Edge AI), we propose a robust approach that enables fast FLI with no degradation of accuracy. This approach couples a recurrent neural network (RNN), which is trained to estimate the fluorescence lifetime directly from raw timestamps without building histograms, to SPAD TCSPC systems, thereby drastically reducing transfer data volumes and hardware resource utilization, and enabling real-time FLI acquisition. We train two variants of the RNN on a synthetic dataset an

SUBMITTER: Lin Y 

PROVIDER: S-EPMC10853568 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature