ArticleBiomedical optics express2019
Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- LoGoReg-Net: local-global feature aggregation regularized unrolling network for functional brain imaging via high-density DOT.Biomedical optics express · 2026Article
- Enhanced model iteration algorithm with graph neural network for diffuse optical tomography.Biomedical optics express · 2024Article
- Unrolled-DOT: an interpretable deep network for diffuse optical tomography.Journal of biomedical optics · 2023Article
- Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography.Biomedical optics express · 2023Article
- Deep-learning based image reconstruction for MRI-guided near-infrared spectral tomography.Optica · 2022Article
- L1-L2 norm regularization via forward-backward splitting for fluorescence molecular tomography.Biomedical optics express · 2021Article
- New nonlocal forward model for diffuse optical tomography.Biomedical optics express · 2019Article
- Automatic 3D Bi-Ventricular Segmentation of Cardiac Images by a Shape-Refined Multi- Task Deep Learning Approach.IEEE transactions on medical imaging · 2019Article
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Authors and funding
5 authors.
Funding
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Abstract
Total variation (TV) is a powerful regularization method that has been widely applied in different imaging applications, but is difficult to apply to diffuse optical tomography (DOT) image reconstruction (inverse problem) due to unstructured discretization of complex geometries, non-linearity of the data fitting and regularization terms, and non-differentiability of the regularization term. We develop several approaches to overcome these difficulties by: i) defining discrete differential operators for TV regularization using both finite element and graph representations; ii) developing an optimization algorithm based on the alternating direction method of multipliers (ADMM) for the non-differentiable and non-linear minimization problem; iii) investigating isotropic and anisotropic variants of TV regularization, and comparing their finite element (FEM)- and graph-based implementations. These approaches are evaluated on experiments on simulated data and real data acquired from a tissue phantom. Our results show that both FEM and graph-based TV regularization is able to accurately reconstruct both sparse and non-sparse distributions without the over-smoothing effect of Tikhonov regularization and the over-sparsifying effect of L
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