High-fidelity Mesoscopic Fluorescence Molecular Tomography based on SSB-Net
Version 2 2023-01-02, 15:45
Version 1 2023-01-02, 15:45
Posted on 2023-01-02 - 15:45
The imaging fidelity of mesoscopic fluorescence molecular tomography (MFMT) in reflective geometry suffers from spatial non-uniformity of measurement sensitivity and ill-posed reconstruction. In this study, we present a spatially adaptive split Bregman network (SSB-Net) to simultaneously overcome the spatial nonuniformity of measurement sensitivity and promote reconstruction sparsity. SSB-Net is derived by unfolding the split Bregman algorithm. In each layer of SSB-Net, residual block and 3D convolution neural networks (3D-CNN) can adaptively learn the spatially non-uniform error compensation, spatially dependent proximal operator, and sparsity transformation. Simulations and experiments show that the proposed SSB-Net enables high-fidelity MFMT tomographic reconstruction of multifluorophores at different positions within a depth of a few millimeters. Our method paves the way for a practical reflection-mode diffuse optical imaging technique.
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Liu, Kaixuan; Jiang, Yuxuan; Li, Wensong; Chen, Haitao; Luo, Qingming; Deng, Yong (2023). High-fidelity Mesoscopic Fluorescence Molecular Tomography based on SSB-Net. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.6335228.v2
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AUTHORS (6)
KL
Kaixuan Liu
YJ
Yuxuan Jiang
WL
Wensong Li
HC
Haitao Chen
QL
Qingming Luo
YD
Yong Deng