Deep Learning-based Temporal Deconvolution for Photon Time-of-Flight Distribution Retrieval

The acquisition of time-of-flight (ToF) of photons has found numerous applications in the biomedical field. Over the last decades, a few strategies have been proposed to deconvolve the temporal instrument response function (IRF) that distorts the experimental time-resolved data. However, these methods require burdensome computational strategies and regularization terms to mitigate noise contributions. Herein, we propose a deep learning model specifically to perform the deconvolution task in fluorescence lifetime imaging (FLI). The model is trained and validated with representative simulated FLI data with the goal of retrieving the true photon ToF distribution. Its performance and robustness are validated with well-controlled \emph{in vitro} experiments using three time-resolved imaging modalities with markedly different temporal IRFs. The model aptitude is further established with \emph{in vivo} preclinical investigation. Overall, these \emph{in vitro} and \emph{in vivo} validations demonstrate the flexibility and accuracy of deep learning model-based deconvolution in time-resolved FLI and diffuse optical imaging.