Multi-channel framelet denoising of diffusion-weighted images

• DOI: 10.1371/journal.pone.0211621
• Publication Date: 2019-02-06T13:38:36Z
• Authors: Geng Chen; Jian Zhang; Yong Zhang; Bin Dong; Dinggang Shen; Pew-Thian Yap
• Keywords: SIGNAL (programming language); Total variation denoising

Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with is reduction signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance image noise reduction. However, TV denoising can result stair-casing effects due to inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame approach for edge-preserving diffusion-weighted (DW) images. Specifically, employ unitary extension principle (UEP) generate frames that are discrete analogues differential operators various orders, which will help avoid effects. Instead each DW separately, collaboratively denoise groups images acquired adjacent gradient directions. addition, introduce very efficient method solving an ℓ0 problem involves only thresholding and trivial inverse problem. We demonstrate effectiveness our qualitatively quantitatively using synthetic real data.