COMPRESSED SENSING MRI WITH BAYESIAN DICTIONARY LEARNING

被引：0

作者：

Ding, Xinghao ^{[1
]}

Paisley, John ^{[2
]}

Huang, Yue ^{[1
]}

Chen, Xianbo ^{[1
]}

Huang, Feng ^{[3
]}

Zhang, Xiao-ping ^{[1
,4
]}

机构：

[1] Xiamen Univ, Dept Commun Engn, Fujian, Peoples R China

[2] Univ Calif Berkeley, Dept EECS, Berkeley, CA 94720 USA

[3] Philips Res China, Shanghai, Peoples R China

[4] Ryerson Univ, Dept Elect & Comp Engn, Toronto, ON, Canada

来源：

2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013) | 2013年

关键词：

compressed sensing; MRI reconstruction; dictionary learning; Bayesian models; IMAGE-RECONSTRUCTION; ALGORITHM;

D O I：

暂无

中图分类号：

TB8 [摄影技术];

学科分类号：

0804 ;

摘要：

We present an inversion algorithm for magnetic resonance images (MRI) that are highly undersampled in k-space. The proposed method incorporates spatial finite differences (total variation) and patch-wise sparsity through in situ dictionary learning. We use the beta-Bernoulli process as a Bayesian prior for dictionary learning, which adaptively infers the dictionary size, the sparsity of each patch and the noise parameters. In addition, we employ an efficient numerical algorithm based on the alternating direction method of multipliers (ADMM). We present empirical results on two MR images.

引用

页码：2319 / 2323

页数：5

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