Edited by Francisco Bezanilla, The University of Chicago, Chicago, IL, and approved October 16, 2019 (received for review July 26, 2019)
Fluorescence lifetime imaging (FLI), although capable of providing powerful biomedical insight, necessitates computationally expensive inverse solvers to obtain parameters of interest, which has limited its broad dissemination, especially in clinical settings. Hence, a real-time, user-friendly approach has the potential to transform this field. Herein, a deep neural network (DNN) architecture named fluorescence lifetime imaging network (FLI-Net) was designed to form spatially resolved quantitative images of complex fluorescence lifetimes without any user-defined parameter input during processing. Thus, FLI-Net is poised to facilitate current lifetime imaging applications, expand its dissemination, and lead to new applications, especially in preclinical and clinical settings.
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.
↵2Present address: Assistive Technology and Medical Devices Research Center, National Science and Technology Development Agency, 12120 Pathum Thani, Thailand.
Author contributions: J.T.S. and X.I. conceived the original idea; J.T.S., R.Y., A.R., M.B., and X.I. designed research; J.T.S., N.S., A.R., J.M., and M.B. performed research; J.T.S., R.Y., N.S., J.M., M.B., and P.Y. contributed new reagents/analytic tools; J.T.S. and N.U. analyzed data; and J.T.S. and X.I. wrote the paper.
The authors declare no competing interest.
This article is a PNAS Direct Submission.
Data deposition: All data is accessible through GitHub (https://github.com/jasontsmith2718/DL4FLI).
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1912707116/-/DCSupplemental.
Published under the PNAS license.
