Abstract
Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.
| Original language | English |
|---|---|
| Article number | e0211621 |
| Journal | PLoS One |
| Volume | 14 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2019 Feb 1 |
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ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)
Cite this
Multi-channel framelet denoising of diffusion-weighted images. / Chen, Geng; Zhang, Jian; Zhang, Yong; Dong, Bin; Shen, Dinggang; Yap, Pew Thian.
In: PLoS One, Vol. 14, No. 2, e0211621, 01.02.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Multi-channel framelet denoising of diffusion-weighted images
AU - Chen, Geng
AU - Zhang, Jian
AU - Zhang, Yong
AU - Dong, Bin
AU - Shen, Dinggang
AU - Yap, Pew Thian
PY - 2019/2/1
Y1 - 2019/2/1
N2 - Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.
AB - Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.
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U2 - 10.1371/journal.pone.0211621
DO - 10.1371/journal.pone.0211621
M3 - Article
C2 - 30726257
AN - SCOPUS:85060518213
VL - 14
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 2
M1 - e0211621
ER -