Spatial resolution improved fluorescence lifetime imaging via deep learning

We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from Fluorescence Lifetime IMaging (FLIM) systems. We first proposed a theoretical method for training neural networks to generate massive semi-synthetic FLIM data with various cellular morphologies, a sizeable dynamic lifetime range, and complex decay components. We then developed a degrading model to obtain LR-HR pairs and created a hybrid neural network, the Spatial Resolution Improved FLIM net (SRI-FLIMnet), to simultaneously estimate fluorescence lifetimes and realize the nonlinear transformation from LR to HR images. The evaluative results demonstrate SRI-FLIMnet’s superior performance in reconstructing spatial information from limited pixel resolution. We also verified SRI-FLIMnet using experimental images of bacterial infected mouse raw macrophage cells. Results show that the proposed data generation method and SRIFLIMnet efficiently achieve superior spatial resolution for FLIM applications. Our study provides a solution for fast obtaining HR FLIM images.