通过利用组稀疏性探索区域低秩结构加速心血管成像
ACCELERATING CARDIOVASCULAR IMAGING BY EXPLOITING REGIONAL LOW-RANK STRUCTURE VIA GROUP SPARSITY.
作者信息
Christodoulou Anthony G, Babacan S Derin, Liang Zhi-Pei
机构信息
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign ; Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign.
Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign.
出版信息
Proc IEEE Int Symp Biomed Imaging. 2012 Dec 31;2012:330-333. doi: 10.1109/ISBI.2012.6235551.
Sparse sampling of (, )-space has proved useful for cardiac MRI. This paper builds on previous work on using partial separability (PS) and spatial-spectral sparsity for high-quality image reconstruction from highly undersampled (, )-space data. This new method uses a more flexible control over the PS-induced low-rank constraint via group-sparse regularization. A novel algorithm is also described to solve the corresponding (1,2)-norm regularized inverse problem. Reconstruction results from simulated cardiovascular imaging data are presented to demonstrate the performance of the proposed method.
事实证明，对(k, t)-空间进行稀疏采样在心脏磁共振成像中很有用。本文基于先前关于利用部分可分性（PS）和空间谱稀疏性从高度欠采样的(k, t)-空间数据进行高质量图像重建的工作。这种新方法通过组稀疏正则化对PS诱导的低秩约束进行了更灵活的控制。还描述了一种新颖的算法来解决相应的(1,2)-范数正则化逆问题。给出了模拟心血管成像数据的重建结果，以证明所提方法的性能。
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