XQ-NLM: Denoising diffusion MRI data via x-q space non-local patch matching

Geng Chen, Yafeng Wu, Dinggang Shen, Pew Thian Yap

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


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,nonlocal 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.,the x-space),despite the fact that diffusion data live in a combined space consisting of the x-space and the q-space (i.e.,the space of wavevectors). In this paper,we propose to extend NLM to both x-space and q-space. We show how patch-matching,as required in NLM,can be performed concurrently in x-q space with the help of azimuthal equidistant projection and rotation invariant features. Extensive experiments on both synthetic and real data confirm that the proposed x-q space NLM (XQ-NLM) outperforms the classic NLM.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsLeo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319467252
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9902 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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