Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI image formation that is based on deep learning (DL) to quantify fluorescence decays simultaneously over a whole image and at fast speeds. We report on a deep neural network (DNN) architecture, named fluorescence lifetime imaging network (FLI-Net) that is designed and trained for different classes of experiments, including visible FLI and near-infrared (NIR) FLI microscopy (FLIM) and NIR gated macroscopy FLI (MFLI). FLI-Net outputs quantitatively the spatially resolved lifetime-based parameters that are typically employed in the field. We validate the utility of the FLI-Net framework by performing quantitative microscopic and preclinical lifetime-based studies across the visible and NIR spectra, as well as across the 2 main data acquisition technologies. These results demonstrate that FLI-Net is well suited to accurately quantify complex fluorescence lifetimes in cells and, in real time, in intact animals without any parameter settings. Hence, FLI-Net paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications ranging from fundamental discoveries in molecular and cellular biology to clinical translation.

Förster resonance energy transfer (FRET) microscopy is used in numerous biophysical and biomedical applications to monitor inter- and intramolecular interactions and conformational changes in the 2–10 nm range. FRET is currently being extended to in vivo optical imaging, its main application being in quantifying drug-target engagement or drug release in animal models of cancer using organic dye or nanoparticle-labeled probes. Herein, we compared FRET quantification using intensity-based FRET (sensitized emission FRET analysis with the three-cube approach using an IVIS imager) and macroscopic fluorescence lifetime (MFLI) FRET using a custom system using a time-gated-intensified charge-coupled device, for small animal optical in vivo imaging. The analytical expressions and experimental protocols required to quantify the product $f_{D}E$ of the FRET efficiency E and the fraction of donor molecules involved in FRET, $f_D$, are described in detail for both methodologies. Dynamic in vivo FRET quantification of transferrin receptor-transferrin binding was acquired in live intact nude mice upon intravenous injection of a near-infrared-labeled transferrin FRET pair and benchmarked against in vitro FRET using hybridized oligonucleotides. Even though both in vivo imaging techniques provided similar dynamic trends for receptor-ligand engagement, we demonstrate that MFLI-FRET has significant advantages. Whereas the sensitized emission FRET approach using the IVIS imager required nine measurements (six of which are used for calibration) acquired from three mice, MFLI-FRET needed only one measurement collected from a single mouse, although a control mouse might be needed in a more general situation. Based on our study, MFLI therefore represents the method of choice for longitudinal preclinical FRET studies such as that of targeted drug delivery in intact, live mice.