Rank-One and Transformed Sparse Decomposition for Dynamic Cardiac MRI.

It is challenging and inspiring for us to achieve high spatiotemporal resolutions in dynamic cardiac magnetic resonance imaging (MRI). In this paper, we introduce two novel models and algorithms to reconstruct dynamic cardiac MRI data from under-sampled k-t space data. In contrast to classical low-r...

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Publicado en:BioMed Research International Vol. 2015; pp. 1 - 8
Autores principales: Xiu, Xianchao, Kong, Lingchen
Formato: diagnostic images equations & formulas research Journal Article
Publicado: Wiley-Blackwell 7/12/2015
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 7/12/2015
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      pub: Wiley-Blackwell
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        atl: Rank-One and Transformed Sparse Decomposition for Dynamic Cardiac MRI.
      aug:
        au:
          Xiu, Xianchao
          Kong, Lingchen
        affil: Department of Applied Mathematics, Beijing Jiaotong University, Beijing 100044, China
      sug:
        subj:
          Heart Diseases Diagnosis
          Magnetic Resonance Imaging
          Software Design
          Models, Statistical
          Computer Simulation
      ab: It is challenging and inspiring for us to achieve high spatiotemporal resolutions in dynamic cardiac magnetic resonance imaging (MRI). In this paper, we introduce two novel models and algorithms to reconstruct dynamic cardiac MRI data from under-sampled k-t space data. In contrast to classical low-rank and sparse model, we use rank-one and transformed sparse model to exploit the correlations in the dataset. In addition, we propose projected alternative direction method (PADM) and alternative hard thresholding method (AHTM) to solve our proposed models. Numerical experiments of cardiac perfusion and cardiac cine MRI data demonstrate improvement in performance.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        Journal Article
      ougenre: Article
    language: English
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