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Noise reduction in diffusion MRI using non-local self-similar information in joint x - q space

Authors
Chen, GengWu, YafengShen, DinggangYap, Pew-Thian
Issue Date
4월-2019
Publisher
ELSEVIER SCIENCE BV
Keywords
Denoising; Diffusion MRI; Non-local means; Patch matching
Citation
MEDICAL IMAGE ANALYSIS, v.53, pp.79 - 94
Indexed
SCIE
SCOPUS
Journal Title
MEDICAL IMAGE ANALYSIS
Volume
53
Start Page
79
End Page
94
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/66403
DOI
10.1016/j.media.2019.01.006
ISSN
1361-8415
Abstract
Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x - q space. Specifically, we define for each point in the x - q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively. (C) 2019 Elsevier B.V. All rights reserved.
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