High-Frequency Subband Compressed Sensing MRI Using Quadruplet Sampling

被引:16
作者
Sung, Kyunghyun [1 ,2 ]
Hargreaves, Brian A. [1 ]
机构
[1] Stanford Univ, Dept Radiol, Stanford, CA 94305 USA
[2] Univ Calif Los Angeles, Dept Radiol Sci, Los Angeles, CA 90095 USA
关键词
image reconstruction; compressed sensing; wavelet transformation; parallel imaging; iterative reconstruction; TREE APPROXIMATION; WAVELET TRANSFORM; K-SPACE; RECONSTRUCTION; ALGORITHMS; RECOVERY; FOCUSS; DOMAIN;
D O I
10.1002/mrm.24592
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
PurposeTo present and validate a new method that formalizes a direct link between k-space and wavelet domains to apply separate undersampling and reconstruction for high- and low-spatial-frequency k-space data. Theory and MethodsHigh- and low-spatial-frequency regions are defined in k-space based on the separation of wavelet subbands, and the conventional compressed sensing problem is transformed into one of localized k-space estimation. To better exploit wavelet-domain sparsity, compressed sensing can be used for high-spatial-frequency regions, whereas parallel imaging can be used for low-spatial-frequency regions. Fourier undersampling is also customized to better accommodate each reconstruction method: random undersampling for compressed sensing and regular undersampling for parallel imaging. ResultsExamples using the proposed method demonstrate successful reconstruction of both low-spatial-frequency content and fine structures in high-resolution three-dimensional breast imaging with a net acceleration of 11-12. ConclusionThe proposed method improves the reconstruction accuracy of high-spatial-frequency signal content and avoids incoherent artifacts in low-spatial-frequency regions. This new formulation also reduces the reconstruction time due to the smaller problem size. Magn Reson Med 70:1306-1318, 2013. (c) 2012 Wiley Periodicals, Inc.
引用
收藏
页码:1306 / 1318
页数:13
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