Spatial resolution improved fluorescence lifetime imaging via deep learning

doi:10.1364/OE.451215

2022-03-22

DOI：10.1364/OE.451215

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摘要

En 中文

We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from fluorescence lifetime imaging (FLIM) systems. We first proposed a theoretical method for training neural networks to generate massive semi-synthetic FLIM data with various cellular morphologies, a sizeable dynamic lifetime range, and complex decay components. We then developed a degrading model to obtain LR-HR pairs and created a hybrid neural network, the spatial resolution improved FLIM net (SRI-FLIMnet) to simultaneously estimate fluorescence lifetimes and realize the nonlinear transformation from LR to HR images. The evaluative results demonstrate SRI-FLIMnet's superior performance in reconstructing spatial information from limited pixel resolution. We also verified SRI-FLIMnet using experimental images of bacterial infected mouse raw macrophage cells. Results show that the proposed data generation method and SRI-FLIMnet efficiently achieve superior spatial resolution for FLLM applications. Our study provides a solution for fast obtaining HR FLIM images. Published by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License.

期刊

Optics Express

IF:

3.3

论文数:

6.1W

被引数:

14.3W

机构

university of strathclyde

学者数:

1.1W

论文数: 1.1W

被引数: 12

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