Multi-channel framelet denoising of diffusion-weighted images

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

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3 Citations (Scopus)


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 languageEnglish
Article numbere0211621
JournalPloS one
Issue number2
Publication statusPublished - 2019 Feb

Bibliographical note

Funding Information:
This work was supported in part by NIH grants (NS093842 to Pew-Thian Yap, EB022880 to Pew-Thian Yap and Dinggang Shen, EB006733 to Dinggang Shen, EB009634 to Dinggang Shen, AG041721 to Dinggang Shen, and MH100217 to Dinggang Shen) and Hunan Provincial Education Department grant (15A066) to Jian Zhang. Bin Dong was supported in part by NSFC 11671022. This work was supported in part by NIH grants (NS093842, EB022880, EB006733, EB009634, AG041721, and MH100217) and Hunan Provincial Education Department grant (15A066). Bin Dong was supported in part by NSFC 11671022.

Publisher Copyright:
© 2019 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

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