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 |
| Publisher | Springer Heidelberg |
| Pages | 49-59 |
| Number of pages | 11 |
| Volume | Part F2 |
| ISBN (Print) | 9783319541297 |
| DOIs | |
| 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 |
|---|---|
| Volume | Part F2 |
| ISSN (Print) | 1612-3786 |
| ISSN (Electronic) | 2197-666X |
Other
| Other | MICCAI Workshop on Computational Diffusion MRI, CDMRI 2016 |
|---|---|
| Country | Greece |
| City | Athens |
| Period | 16/10/17 → 16/10/21 |
Fingerprint
ASJC Scopus subject areas
- Modelling and Simulation
- Geometry and Topology
- Computer Graphics and Computer-Aided Design
- Applied Mathematics
Cite this
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 proceeding › Conference contribution
}
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
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.
UR - http://www.scopus.com/inward/record.url?scp=85019730383&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85019730383&partnerID=8YFLogxK
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
VL - Part F2
T3 - Mathematics and Visualization
SP - 49
EP - 59
BT - Computational Diffusion MRI - MICCAI Workshop
PB - Springer Heidelberg
ER -