ArticleComputational and mathematical methods in medicine2015
Reconstruction Method for Optical Tomography Based on the Linearized Bregman Iteration with Sparse Regularization.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Multiscale local sparsity and prior learning algorithm for Cherenkov-excited luminescence scanned tomography reconstruction.Applied optics · 2025Article
- L1-L2 norm regularization via forward-backward splitting for fluorescence molecular tomography.Biomedical optics express · 2021Article
- Bioluminescence tomography with structural information estimated via statistical mouse atlas registration.Biomedical optics express · 2018Article
- Comparison of source localization techniques in diffuse optical tomography for fNIRS application using a realistic head model.Biomedical optics express · 2018Article
- Mixed Total Variation andComputational and mathematical methods in medicine · 2017Article
- LMolecular imaging and biology · 2016Article
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Authors and funding
5 authors.
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Abstract
Optical molecular imaging is a promising technique and has been widely used in physiology, and pathology at cellular and molecular levels, which includes different modalities such as bioluminescence tomography, fluorescence molecular tomography and Cerenkov luminescence tomography. The inverse problem is ill-posed for the above modalities, which cause a nonunique solution. In this paper, we propose an effective reconstruction method based on the linearized Bregman iterative algorithm with sparse regularization (LBSR) for reconstruction. Considering the sparsity characteristics of the reconstructed sources, the sparsity can be regarded as a kind of a priori information and sparse regularization is incorporated, which can accurately locate the position of the source. The linearized Bregman iteration method is exploited to minimize the sparse regularization problem so as to further achieve fast and accurate reconstruction results. Experimental results in a numerical simulation and in vivo mouse demonstrate the effectiveness and potential of the proposed method.
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