Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions

Jiaju Cheng; Peng Zhang; Fei Liu; Jie Liu; Hui Hui; Jie Tian; Jianwen Luo

doi:10.1364/BOE.466349

Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions

Biomed Opt Express. 2022 Aug 11;13(9):4693-4705. doi: 10.1364/BOE.466349. eCollection 2022 Sep 1.

Authors

Jiaju Cheng¹, Peng Zhang^{2

3}, Fei Liu⁴, Jie Liu², Hui Hui³, Jie Tian^{3

5}, Jianwen Luo¹

Affiliations

¹ Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, China.
² Department of Biomedical Engineering, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.
³ CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
⁴ Beijing Advanced Information and Industrial Technology Research Institute, Beijing Information Science and Technology University, Beijing 100192, China.
⁵ Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing 100191, China.

Abstract

A time-domain fluorescence molecular tomography in reflective geometry (TD-rFMT) has been proposed to circumvent the penetration limit and reconstruct fluorescence distribution within a 2.5-cm depth regardless of the object size. In this paper, an end-to-end encoder-decoder network is proposed to further enhance the reconstruction performance of TD-rFMT. The network reconstructs both the fluorescence yield and lifetime distributions directly from the time-resolved fluorescent signals. According to the properties of TD-rFMT, proper noise was added to the simulation training data and a customized loss function was adopted for self-supervised and supervised joint training. Simulations and phantom experiments demonstrate that the proposed network can significantly improve the spatial resolution, positioning accuracy, and accuracy of lifetime values.