RT Journal Article
SR Electronic
T1 Ultra-fast fit-free analysis of complex fluorescence lifetime imaging via deep learning
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 523928
DO 10.1101/523928
A1 Jason T. Smith
A1 Ruoyang Yao
A1 Nattawut Sinsuebphon
A1 Alena Rudkouskaya
A1 Joseph Mazurkiewicz
A1 Margarida Barroso
A1 Pingkun Yan
A1 Xavier Intes
YR 2019
UL http://biorxiv.org/content/early/2019/01/17/523928.abstract
AB 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 novel fit-free approach in FLI image formation that is based on Deep Learning (DL) to quantify complex fluorescence decays simultaneously over a whole image and at ultra-fast speeds. Our deep neural network (DNN), named FLI-Net, is designed and model-based trained to provide all lifetime-based parameters that are typically employed in the field. We demonstrate the accuracy and generalizability of FLI-Net by performing quantitative microscopic and preclinical experimental lifetime-based studies across the visible and NIR spectra, as well as across the two main data acquisition technologies. Our results demonstrate that FLI-Net is well suited to quantify complex fluorescence lifetimes, accurately, in real time in cells and intact animals without any parameter settings. Hence, it paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications, especially in clinical settings.