Self-supervised deep learning enables robust fluorescence lifetime estimation with limited photons

This is the authors' abstract. We don't add key points for this paper.

Fluorescence lifetime imaging microscopy (FLIM) provides valuable insights into molecular interactions and states in complex cellular environments. Conventional FLIM analysis methods struggle with accurate lifetime estimation with low photons-per-pixel (PPP). We propose DeepFLR, a self-supervised deep learning framework for robust FLIM signal restoration with limited photons. By exploiting the spatiotemporal dependencies of FLIM signals, DeepFLR reconstructs the fluorescence decay curves, leading to accurate lifetime estimations using existing lifetime estimation methods. The results demonstrate that DeepFLR enables reliable lifetime estimation with less than 10 PPP for a diverse set of biological samples. The proposed approach significantly reduces the photon budget of FLIM and opens up numerous low-light FLIM applications.