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

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

doi:10.1007/978-3-319-46726-9_68

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

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

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

17 Scopus citations

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,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 language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Editors	Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal, Sebastian Ourselin
Publisher	Springer Verlag
Pages	587-595
Number of pages	9
ISBN (Print)	9783319467252
DOIs	https://doi.org/10.1007/978-3-319-46726-9_68
State	Published - 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9902 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-319-46726-9_68

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Chen, G., Wu, Y., Shen, D., & Yap, P. T. (2016). XQ-NLM: Denoising diffusion MRI data via x-q space non-local patch matching. In L. Joskowicz, M. R. Sabuncu, W. Wells, G. Unal, & S. Ourselin (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (pp. 587-595). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46726-9_68

Chen, Geng ; Wu, Yafeng ; Shen, Dinggang et al. / XQ-NLM : Denoising diffusion MRI data via x-q space non-local patch matching. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. editor / Leo Joskowicz ; Mert R. Sabuncu ; William Wells ; Gozde Unal ; Sebastian Ourselin. Springer Verlag, 2016. pp. 587-595 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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,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.",

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Chen, G, Wu, Y, Shen, D & Yap, PT 2016, XQ-NLM: Denoising diffusion MRI data via x-q space non-local patch matching. in L Joskowicz, MR Sabuncu, W Wells, G Unal & S Ourselin (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9902 LNCS, Springer Verlag, pp. 587-595. https://doi.org/10.1007/978-3-319-46726-9_68

XQ-NLM: Denoising diffusion MRI data via x-q space non-local patch matching. / Chen, Geng; Wu, Yafeng; Shen, Dinggang et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. ed. / Leo Joskowicz; Mert R. Sabuncu; William Wells; Gozde Unal; Sebastian Ourselin. Springer Verlag, 2016. p. 587-595 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T2 - Denoising diffusion MRI data via x-q space non-local patch matching

AU - Chen, Geng

AU - Wu, Yafeng

AU - Shen, Dinggang

AU - Yap, Pew Thian

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

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N2 - 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.

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PB - Springer Verlag

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Chen G, Wu Y, Shen D, Yap PT. XQ-NLM: Denoising diffusion MRI data via x-q space non-local patch matching. In Joskowicz L, Sabuncu MR, Wells W, Unal G, Ourselin S, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Springer Verlag. 2016. p. 587-595. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46726-9_68

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

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