Multi-channel framelet denoising of diffusion-weighted images

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.

Noise reduction in diffusion MRI using non-local self-similar information in jointx−qspace

“…A straightforward means to improve SNR is by repeating and averaging scans (Johansen-Berg and Behrens, 2013), which however inevitably prolongs acquisition times and is hence impractical in clinical settings. In view of this, post-acquisition denoising methods have been widely adopted (Wiest-Daesslé et al, 2007, 2008; Descoteaux et al, 2008; Becker et al, 2012; Manjón et al, 2013; Becker et al, 2014; Lam et al, 2014; Yap et al, 2014; Varadarajan and Haldar, 2015; Veraart et al, 2016; St-Jean et al, 2016; Chen et al, 2019). Among existing methods, non-local means (NLM) (Buades et al, 2005) has been shown to be particularly good at preserving edges when reducing noise.…”