ADAPTIVE SAMPLING AND WAVELET TREE BASED COMPRESSIVE SENSING FOR MRI RECONSTRUCTION

被引：0

作者：

Zhang, Qieshi ^{[1
,2
]}

Zhang, Jun ^{[3
,4
]}

Kamata, Sei-ichiro ^{[5
]}

机构：

[1] Shaanxi Normal Univ, Minist Educ, Key Lab Modern Teaching Technol, Xian, Peoples R China

[2] Shaanxi Normal Univ, Sch Comp Sci, Xian, Peoples R China

[3] Xian Serv Stress Engn Technol Co Ltd, Xian, Peoples R China

[4] Waseda Univ, Informat Prod & Syst Res Ctr, Tokyo, Japan

[5] Waseda Univ, Grad Sch Informat Prod & Syst, Tokyo, Japan

来源：

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

关键词：

Magnetic Resonance Imaging (MRI); k-space; compressive sensing (CS); Wavelet tree; IMAGE-RECONSTRUCTION;

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：

Magnetic Resonance Imaging (MRI) has been widely used in medical diagnose because of its non-invasive manner and excellent depiction of soft-tissue changes. Recently, the compressive sensing (CS) theory has been applied to reconstruct the MR image from highly down-sampled k-space data, which can reduce the scanning duration. To obtain useful information as much as possible with the same sampling rate, a weighted sampling strategy is studied. Moreover, based on the advantage of CS, a Wavelet tree based reconstruction approach is proposed. The experimental results demonstrate that the proposed method is preferable to other methods.

引用

页码：2524 / 2528

页数：5

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