Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging.
Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accura...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 8 |
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| Autores principales: | , , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
9/25/2016
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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=118314042&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118314042 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 9/25/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 118314042 118314042 118314042 10.1155/2016/2860643 118314042 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Two-Layer Tight Frame Sparsifying Model for Compressed Sensing Magnetic Resonance Imaging. aug: au: Wang, Shanshan Liu, Jianbo Peng, Xi Dong, Pei Liu, Qiegen Liang, Dong affil: Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, Guangdong 518055, China sug: subj: Magnetic Resonance Imaging Methods Algorithms Validity Phantoms, Imaging Models, Statistical Brain Comparative Studies Descriptive Statistics Human Funding Source ab: Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame. The two-layer sparsifying and adaptive learning nature of TRIMS has enabled accurate MR reconstruction from highly undersampled data with efficiency. To solve the reconstruction problem, a three-level Bregman numerical algorithm is developed. The proposed approach has been compared to three state-of-the-art methods over scanned physical phantom and in vivo MR datasets and encouraging performances have been achieved. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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