Recent Computational Advances in Denoising for Magnetic Resonance Diffusional Kurtosis Imaging (DKI)

Abstract

Magnetic resonance imaging (MRI) is widely used in clinical
practice and medical research for the assessment of disease. Magnetic
resonance diffusional kurtosis imaging (DKI) is a specific MRI
technique that is useful for quantifying microstructural properties of
biological tissues, particularly in brain. However, images derived with
DKI can be sensitive to noise, as the MRI sequences needed for DKI
strongly attenuate the signal. To mitigate this inherent noise sensitivity
of DKI, advanced denoising methods maybe applied. Although a variety
of denoising approaches have been considered in the broad context
of MRI, the specific performance of these methods for DKI has not yet
been thoroughly investigated. In this review, we examine three different
denoising strategies for DKI—Gaussian filtering, non-local means filtering,
and a local principal components analysis technique. These three
denoising methods are compared qualitatively in terms of their abilities
to increase image fidelity and to remove noise bias for the DKI-derived
parametric maps.