Affiliation:
1. Huazhong University of Science and Technology
2. Hainan University
Abstract
In fluorescence diffuse optical tomography (fDOT), the quality of reconstruction is severely limited by mismodeling and ill-posedness of inverse problems. Although data-driven deep learning methods improve the quality of image reconstruction, the network architecture lacks interpretability and requires a lot of data for training. We propose an interpretable model-driven projected gradient descent network (MPGD-Net) to improve the quality of fDOT reconstruction using only a few training samples. MPGD-Net unfolds projected gradient descent into a novel deep network architecture that is naturally interpretable. Simulation and in vivo experiments show that MPGD-Net greatly improves the fDOT reconstruction quality with superior generalization ability.
Funder
National Science and Technology Innovation 2030 Major Program
National Natural Science Foundation of China
Independent Innovation Fund of WNLO
Subject
Atomic and Molecular Physics, and Optics
Cited by
7 articles.
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