title: XQ-NLM: Denoising Diffusion MRI Data viax-qSpace Non-Local Patch Matching.
abstract: Noise is a major issue influencing quantitative analysis in diffusion MRI. The effects of noise can be reduced by repeated acquisitions, but this leads to long acquisition times that can be unrealistic in clinical settings. For this reason, post-acquisition denoising methods have been widely used to improve SNR. Among existing methods, non-local means (NLM) has been shown to produce good image quality with edge preservation. However, currently the application of NLM to diffusion MRI has been mostly focused on the spatial space (i.e., thex-space), despite the fact that diffusion data live in a combined space consisting of thex-space and theq-space (i.e., the space of wavevectors). In this paper, we propose to extend NLM to bothx-space andq-space. We show how patch-matching, as required in NLM, can be performed concurrently inx-qspace with the help of azimuthal equidistant projection and rotation invariant features. Extensive experiments on both synthetic and real data confirm that the proposedx-qspace NLM (XQ-NLM) outperforms the classic NLM.
authors.affiliation: Data Processing Center, Northwestern Polytechnical University, Xi'an, China; Department of Radiology and BRIC, University of North Carolina, Chapel Hill, U.S.A.
authors.forename: Geng
authors.initials: G
authors.lastname: Chen
authors.affiliation: Data Processing Center, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Yafeng
authors.initials: Y
authors.lastname: Wu
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, U.S.A.
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
authors.affiliation: Department of Radiology and BRIC, University of North Carolina, Chapel Hill, U.S.A.
authors.forename: Pew-Thian
authors.initials: PT
authors.lastname: Yap
mesh_terms: D000465:Algorithms; D066264:Datasets as Topic; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio