ACCELERATED DYNAMIC MRI USING STRUCTURED LOW RANK MATRIX COMPLETION

被引：0

作者：

Balachandrasekaran, Arvind ^{[1
]}

Ongie, Greg ^{[2
]}

Jacob, Mathews ^{[1
]}

机构：

[1] Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USA

[2] Univ Iowa, Dept Math, Iowa City, IA 52242 USA

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2016年

关键词：

dynamic MRI; structured low rank; smoothness regularization;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

We introduce a fast structured low-rank matrix completion algorithm with low memory & computational demand to recover the dynamic MRI data from undersampled measurements. The 3-D dataset is modeled as a piecewise smooth signal, whose discontinuities are localized to the zero sets of a bandlimited function. We show that a structured matrix corresponding to convolution with the Fourier coefficients of the signal derivatives is highly low-rank. This property enables us to recover the signal from undersampled measurements. The application of this scheme in dynamic MRI shows significant improvement over state of the art methods.

引用

页码：1858 / 1862

页数：5

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