Multi-channel framelet denoising of diffusion-weighted images
Geng Chen,
Jian Zhang,
Yong Zhang,
Bin Dong,
Dinggang Shen and
Pew-Thian Yap
PLOS ONE, 2019, vol. 14, issue 2, 1-19
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.
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0211621
DOI: 10.1371/journal.pone.0211621
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