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 proceedingConference 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 languageEnglish (US)
Title of host publicationComputational Diffusion MRI - MICCAI Workshop
PublisherSpringer Heidelberg
Pages49-59
Number of pages11
VolumePart F2
ISBN (Print)9783319541297
DOIs
StatePublished - 2017
Externally publishedYes
EventMICCAI Workshop on Computational Diffusion MRI, CDMRI 2016 - Athens, Greece
Duration: Oct 17 2016Oct 21 2016

Publication series

NameMathematics and Visualization
VolumePart F2
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X

Other

OtherMICCAI Workshop on Computational Diffusion MRI, CDMRI 2016
CountryGreece
CityAthens
Period10/17/1610/21/16

Fingerprint

Thresholding
Denoising
Inverse problems
Wavelet Frames
Tight Frame
Extension Principle
Synthetic Data
Quantitative Analysis
Differential operator
Inverse Problem
Trivial
Gradient
Analogue
Chemical analysis
Decrease
Experiments
Experiment

ASJC Scopus subject areas

  • Modeling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics

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 Computational Diffusion MRI - MICCAI Workshop (Vol. Part F2, pp. 49-59). (Mathematics and Visualization; Vol. Part F2). 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; Dong, Bin; Shen, Dinggang; Yap, Pew Thian.

Computational Diffusion MRI - MICCAI Workshop. Vol. Part F2 Springer Heidelberg, 2017. p. 49-59 (Mathematics and Visualization; Vol. Part F2).

Research output: Chapter in Book/Report/Conference proceedingConference 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 Computational Diffusion MRI - MICCAI Workshop. vol. Part F2, Mathematics and Visualization, vol. Part F2, Springer Heidelberg, pp. 49-59, MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016, Athens, Greece, 10/17/16. https://doi.org/10.1007/978-3-319-54130-3_4
Zhang J, Chen G, Zhang Y, Dong B, Shen D, Yap PT. Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets. In Computational Diffusion MRI - MICCAI Workshop. Vol. Part F2. Springer Heidelberg. 2017. p. 49-59. (Mathematics and Visualization). https://doi.org/10.1007/978-3-319-54130-3_4
Zhang, Jian ; Chen, Geng ; Zhang, Yong ; Dong, Bin ; Shen, Dinggang ; Yap, Pew Thian. / Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets. Computational Diffusion MRI - MICCAI Workshop. Vol. Part F2 Springer Heidelberg, 2017. pp. 49-59 (Mathematics and Visualization).
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