Noise Reduction in Diffusion MRI Using Non-Local Self-Similar Information in Joint x-q Space

TL;DR

This paper introduces an advanced noise reduction method for diffusion MRI that leverages non-local self-similar information in joint x-q space, improving accuracy over traditional spatial-only approaches.

Contribution

The paper extends non-local means to the combined x-q space in diffusion MRI, enabling more effective noise reduction by matching patches across both spatial and diffusion domains.

Findings

Outperforms existing methods in synthetic and real data tests.

Effectively reduces noise in high b-value diffusion MRI.

Enhances white matter structure visualization.

Abstract

Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated acquisition, and real data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.