Wavelet Tree Support Detection for Compressed Sensing MRI Reconstruction

被引：13

作者：

Deka, Bhabesh ^{[1
]}

Datta, Sumit ^{[1
]}

Handique, Sanjeev ^{[2
]}

机构：

[1] Tezpur Univ, Dept Elect & Commun Engn, Tezpur 784028, India

[2] GNRC, Dept Radiol & Imaging, Gauhati 781006, India

来源：

IEEE SIGNAL PROCESSING LETTERS | 2018年 / 25卷 / 05期

关键词：

Compressed sensing magnetic resonance imaging (CS-MRI); hidden Markov tree (HMT); just noticeable difference scanning (JNDS); wavelet tree support; HIDDEN MARKOV-MODELS; RESONANCE IMAGE-RECONSTRUCTION; SIGNAL RECONSTRUCTION; SPARSITY;

D O I：

10.1109/LSP.2018.2824251

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：

A priori knowledge of the signal/ image support based on its statistical and structural information in the transformed domain improves the quality of compressed sensing (CS) reconstruction. Hidden Markov tree models the wavelet domain support of magnetic resonance images obtained from undersampled k-space data very well. With the support information, parent-child pairs in the wavelet tree are detected accurately, and hence, iterative regularization problems for CS-based magnetic resonance imaging reconstruction are solved with high throughputs. Simulation results with artificial and real MRI datasets show significant improvements over existing methods in terms of just noticeable difference scanning besides other objective and subjective metrics.

引用

页码：730 / 734

页数：5

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