TY - GEN
T1 - Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets
AU - Zhang, Jian
AU - Chen, Geng
AU - Zhang, Yong
AU - Dong, Bin
AU - Shen, Dinggang
AU - Yap, Pew Thian
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.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017
Y1 - 2017
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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U2 - 10.1007/978-3-319-54130-3_4
DO - 10.1007/978-3-319-54130-3_4
M3 - Conference contribution
AN - SCOPUS:85019730383
SN - 9783319541297
T3 - Mathematics and Visualization
SP - 49
EP - 59
BT - Computational Diffusion MRI - MICCAI Workshop
A2 - Fuster, Andrea
A2 - Rathi, Yogesh
A2 - Reisert, Marco
A2 - Kaden, Enrico
A2 - Ghosh, Aurobrata
PB - Springer Heidelberg
T2 - MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016
Y2 - 17 October 2016 through 21 October 2016
ER -