Sparse sampling of (>k, t)-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 (>k, t)-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.
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机译：已证明（> k ，t）空间的稀疏采样对心脏MRI有用。本文基于先前的工作，即利用部分可分离性（PS）和空间光谱稀疏性从高度欠采样（> k ，t）空间数据重建高质量图像。这种新方法通过组稀疏正则化对PS引起的低秩约束使用更灵活的控制。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。提出了从模拟心血管成像数据重建的结果，以证明该方法的性能。
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