FURTHER DEVELOPMENT OF IMAGE RECONSTRUCTION FROM HIGHLY UNDERSAMPLED (k, t)-SPACE DATA WITH JOINT PARTIAL SEPARABILITY AND SPARSITY CONSTRAINTS

被引：0
|
作者：
Zhao, Bo [1 ]
Haldar, Justin P.
Christodoulou, Anthony G.
Liang, Zhi-Pei
机构：
[1] Univ Illinois, Dept Elect & Comp Engn, 1406 W Green St, Urbana, IL 61801 USA
来源：
2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO | 2011年
关键词：
Dynamic MRI; Partial Separability; Sparsity; Low-rank Matrices; Total Variation; Half-quadratic Regularization;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Joint use of partial separability (PS) and spatial-spectral sparsity constraints has previously been demonstrated useful for image reconstruction from undersampled data. This paper extends our early work in this area by proposing a new method for jointly enforcing the PS and spatial total variation (TV) constraints for dynamic MR image reconstruction. An algorithm is also described to solve the underlying optimization problem efficiently. The proposed method has been validated using simulated cardiac imaging data, with the expected capability to reduce image artifacts and reconstruction noise.
引用
收藏
页码：1593 / 1596
页数：4
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