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Denoise diffusion-weighted images using higher-order singular value decomposition.

Abstract：

:Noise usually affects the reliability of quantitative analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI), especially at high b-values and/or high spatial resolution. Higher-order singular value decomposition (HOSVD) has recently emerged as a simple, effective, and adaptive transform to exploit sparseness within multidimensional data. In particular, the patch-based HOSVD denoising has demonstrated superb performance when applied to T1-, T2-, and proton density-weighted MRI data. In this study, we aim to investigate the feasibility of denoising DW data using the HOSVD transform. With the low signal-to-noise ratio in typical DW data, the patch-based HOSVD denoising suffers from stripe artifacts in homogeneous regions because of the HOSVD bases learned from the noisy patches. To address this problem, we propose a novel denoising method. It first introduces a global HOSVD-based denoising as a prefiltering stage to guide the subsequent patch-based HOSVD denoising stage. The HOSVD bases from the patch groups in prefiltered images are then used to transform the noisy patch groups in original DW data. Experiments were performed using simulated and in vivo DW data. Results show that the proposed method significantly reduces stripe artifacts compared with conventional patch-based HOSVD denoising methods, and outperforms two state-of-the-art denoising methods in terms of denoising quality and diffusion parameters estimation.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Zhang X,Peng J,Xu M,Yang W,Zhang Z,Guo H,Chen W,Feng Q,Wu EX,Feng Y

doi

10.1016/j.neuroimage.2017.04.017

subject

Has Abstract

pub_date

2017-08-01 00:00:00

pages

128-145

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(17)30309-9

journal_volume

156

pub_type

杂志文章

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