Fast and Robust Reconstruction for Fluorescence Molecular Tomography via L1-2 Regularization.
Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L1 minimization may not produce the sparsest solution. In th...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 10 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/6/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120012580&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120012580 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 12/6/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 120012580 120012580 120012580 10.1155/2016/5065217 120012580 ppf: 1 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Fast and Robust Reconstruction for Fluorescence Molecular Tomography via L1-2 Regularization. aug: au: Zhang, Haibo Geng, Guohua Wang, Xiaodong Qu, Xuan Hou, Yuqing He, Xiaowei affil: School of Information Sciences and Technology, Northwest University, Xi’an, Shaanxi 710027, China sug: subj: Tomography, Optical Fluorescence Polarization Anatomy Magnetic Resonance Imaging Imaging, Three-Dimensional Animal Studies Mice Funding Source ab: Sparse reconstruction inspired by compressed sensing has attracted considerable attention in fluorescence molecular tomography (FMT). However, the columns of system matrix used for FMT reconstruction tend to be highly coherent, which means L1 minimization may not produce the sparsest solution. In this paper, we propose a novel reconstruction method by minimization of the difference of L1 and L2 norms. To solve the nonconvex L1-2 minimization problem, an iterative method based on the difference of convex algorithm (DCA) is presented. In each DCA iteration, the update of solution involves an L1 minimization subproblem, which is solved by the alternating direction method of multipliers with an adaptive penalty. We investigated the performance of the proposed method with both simulated data and in vivo experimental data. The results demonstrate that the DCA for L1-2 minimization outperforms the representative algorithms for L1, L2, L1/2, and L0 when the system matrix is highly coherent. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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