Enhancing Fluorescence Lifetime Imaging With Differential Transformer.

Conventional analytical methods for fluorescence lifetime imaging (FLI) require the instrument response function (IRF) alongside temporal fluorescence decay to accurately estimate lifetime parameters. While the IRF remains spatially and temporally invariant in microscopy, it exhibits significant pixel-wise spatial variation and temporal offsets at mesoscopic and macroscopic scales due to sample size and surface topography. Existing deep learning approaches, trained on limited synthetic or simplified experimental datasets, often fail to generalize to complex biological or in vivo imaging. Here, we introduce MFliNet, a deep learning framework working in accordance with the physics of photon time-of-flight deconvolution for accurate multi-exponential decay parameter estimation. Utilizing a Differential Transformer encoder-decoder architecture, MFliNet jointly processes temporal fluorescence decay and corresponding IRF inputs to correct topographical distortions in photon arrival distributions. Validation with tissue-mimicking phantoms and preclinical tumor models demonstrates exceptional robustness and precision, enabling reliable, real-time macroscopic FLI for complex biological and intraoperative applications.