K-SVD FOR HARDI DENOISING

被引：0

作者：

Patel, Vishal ^{[1
]}

Shi, Yonggang ^{[1
]}

Thompson, Paul M. ^{[1
]}

Toga, Arthur W. ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Lab Neuro Imaging, Los Angeles, CA 90095 USA

来源：

2011 8TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO | 2011年

关键词：

Magnetic resonance imaging; diffusion tensor imaging; noise reduction; algorithms; brain;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Noise is an important concern in high-angular resolution diffusion imaging studies because it can lead to errors in downstream analyses of white matter structure. To address this issue, we investigate a new approach for denoising diffusion-weighted data sets based on the K-SVD algorithm. We analyze its characteristics using both simulated and biological data and compare its performance with existing methods. Our results show that K-SVD provides robust and effective noise reduction and is practical for use in high-volume applications.

引用

页码：1805 / 1808

页数：4

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