降噪
磁共振弥散成像
冗余(工程)
计算机科学
噪音(视频)
扩散
算法
信号(编程语言)
人工智能
模式识别(心理学)
主成分分析
协方差
协方差矩阵
信号处理
数学
统计
物理
图像(数学)
磁共振成像
操作系统
放射科
热力学
电信
医学
程序设计语言
雷达
作者
Jelle Veraart,Dmitry S. Novikov,Daan Christiaens,Benjamin Ades‐Aron,Jan Sijbers,Els Fieremans
出处
期刊:NeuroImage
[Elsevier]
日期:2016-11-01
卷期号:142: 394-406
被引量:1134
标识
DOI:10.1016/j.neuroimage.2016.08.016
摘要
We introduce and evaluate a post-processing technique for fast denoising of diffusion-weighted MR images. By exploiting the intrinsic redundancy in diffusion MRI using universal properties of the eigenspectrum of random covariance matrices, we remove noise-only principal components, thereby enabling signal-to-noise ratio enhancements. This yields parameter maps of improved quality for visual, quantitative, and statistical interpretation. By studying statistics of residuals, we demonstrate that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail. Furthermore, we achieve improved precision in the estimation of diffusion parameters and fiber orientations in the human brain without compromising the accuracy and spatial resolution.
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