Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets

Jian Zhang; Geng Chen; Yong Zhang; Bin Dong; Dinggang Shen; Pew Thian Yap

doi:10.1007/978-3-319-54130-3_4

Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets

Jian Zhang, Geng Chen, Yong Zhang, Bin Dong, Dinggang Shen, Pew Thian Yap

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

Abstract

Noise in diffusion-weighted (DW) images increases the complexity of quantitative analysis and decreases the reliability of inferences. Hence, to improve analysis, it is often desirable to remove noise and at the same time preserve relevant image features. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of DW images. Our approach (1) employs the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders; (2) introduces a very efficient method for solving an ₀ denoising problem that involves only thresholding and solving a trivial inverse problem; and (3) groups DW images acquired with neighboring gradient directions for collaborative denoising. Experiments using synthetic data with noncentral chi noise and real data with repeated scans confirm that our method yields superior performance compared with denoising using state-of-the-art methods such as non-local means.

Original language	English
Title of host publication	Computational Diffusion MRI - MICCAI Workshop
Editors	Andrea Fuster, Yogesh Rathi, Marco Reisert, Enrico Kaden, Aurobrata Ghosh
Publisher	Springer Heidelberg
Pages	49-59
Number of pages	11
ISBN (Print)	9783319541297
DOIs	https://doi.org/10.1007/978-3-319-54130-3_4
Publication status	Published - 2017
Externally published	Yes
Event	MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016 - Athens, Greece Duration: 2016 Oct 17 → 2016 Oct 21

Publication series

Name	Mathematics and Visualization
ISSN (Print)	1612-3786
ISSN (Electronic)	2197-666X

Other

Other	MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016
Country/Territory	Greece
City	Athens
Period	16/10/17 → 16/10/21

ASJC Scopus subject areas

Modelling and Simulation
Geometry and Topology
Computer Graphics and Computer-Aided Design
Applied Mathematics

Access to Document

10.1007/978-3-319-54130-3_4

Cite this

Zhang, J., Chen, G., Zhang, Y., Dong, B., Shen, D., & Yap, P. T. (2017). Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets. In A. Fuster, Y. Rathi, M. Reisert, E. Kaden, & A. Ghosh (Eds.), Computational Diffusion MRI - MICCAI Workshop (pp. 49-59). (Mathematics and Visualization). Springer Heidelberg. https://doi.org/10.1007/978-3-319-54130-3_4

Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets. / Zhang, Jian; Chen, Geng; Zhang, Yong et al.

Computational Diffusion MRI - MICCAI Workshop. ed. / Andrea Fuster; Yogesh Rathi; Marco Reisert; Enrico Kaden; Aurobrata Ghosh. Springer Heidelberg, 2017. p. 49-59 (Mathematics and Visualization).

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

Zhang, J, Chen, G, Zhang, Y, Dong, B, Shen, D & Yap, PT 2017, Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets. in A Fuster, Y Rathi, M Reisert, E Kaden & A Ghosh (eds), Computational Diffusion MRI - MICCAI Workshop. Mathematics and Visualization, Springer Heidelberg, pp. 49-59, MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016, Athens, Greece, 16/10/17. https://doi.org/10.1007/978-3-319-54130-3_4

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abstract = "Noise in diffusion-weighted (DW) images increases the complexity of quantitative analysis and decreases the reliability of inferences. Hence, to improve analysis, it is often desirable to remove noise and at the same time preserve relevant image features. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of DW images. Our approach (1) employs the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders; (2) introduces a very efficient method for solving an 0 denoising problem that involves only thresholding and solving a trivial inverse problem; and (3) groups DW images acquired with neighboring gradient directions for collaborative denoising. Experiments using synthetic data with noncentral chi noise and real data with repeated scans confirm that our method yields superior performance compared with denoising using state-of-the-art methods such as non-local means.",

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N1 - Funding Information: This work was supported in part by NIH grants (NS093842, EB006733, EB009634, AG041721, MH100217, and AA012388) and Hunan Provincial Education Department grant (15A066). Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

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N2 - Noise in diffusion-weighted (DW) images increases the complexity of quantitative analysis and decreases the reliability of inferences. Hence, to improve analysis, it is often desirable to remove noise and at the same time preserve relevant image features. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of DW images. Our approach (1) employs the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders; (2) introduces a very efficient method for solving an 0 denoising problem that involves only thresholding and solving a trivial inverse problem; and (3) groups DW images acquired with neighboring gradient directions for collaborative denoising. Experiments using synthetic data with noncentral chi noise and real data with repeated scans confirm that our method yields superior performance compared with denoising using state-of-the-art methods such as non-local means.

AB - Noise in diffusion-weighted (DW) images increases the complexity of quantitative analysis and decreases the reliability of inferences. Hence, to improve analysis, it is often desirable to remove noise and at the same time preserve relevant image features. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of DW images. Our approach (1) employs the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders; (2) introduces a very efficient method for solving an 0 denoising problem that involves only thresholding and solving a trivial inverse problem; and (3) groups DW images acquired with neighboring gradient directions for collaborative denoising. Experiments using synthetic data with noncentral chi noise and real data with repeated scans confirm that our method yields superior performance compared with denoising using state-of-the-art methods such as non-local means.

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Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets

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