A Non-Local Conventional Approach for Noise Removal in 3D MRI

TL;DR

This paper introduces a novel non-local filtering method for 3D MRI noise removal that leverages data redundancy and self-similarity, outperforming existing filters on synthetic and clinical data.

Contribution

The proposed approach enhances traditional local filtering by incorporating non-local self-similarity, improving noise removal in Rician noise models for 3D MRI.

Findings

Outperforms existing denoising filters on synthetic data

Effective on clinical MRI data with Rician noise

Utilizes data redundancy and self-similarity for improved results

Abstract

In this paper, a filtering approach for the 3D magnetic resonance imaging (MRI) assuming a Rician model for noise is addressed. Our denoising method is based on the Conventional Approach (CA) proposed to deal with the noise issue in the squared domain of the acquired magnitude MRI, where the noise distribution follows a Chi-square model rather than the Rician one. In the CA filtering method, the local samples around each voxel is used to estimate the unknown signal value. Intrinsically, such a method fails to achieve the best results where the underlying signal values have different statistical properties. On the contrary, our proposal takes advantage of the data redundancy and self-similarity properties of real MR images to improve the noise removal performance. In other words, in our approach, the statistical momentums of the given 3D MR volume are first calculated to explore the similar patches inside a defined search volume. Then, these patches are put together to obtain the noise-free value for each voxel under processing. The experimental results on the synthetic as well as the clinical MR data show our proposed method outperforms the other compared denoising filters.