Reconstructing MRI Parameters Using a Noncentral Chi Noise Model

TL;DR

This paper improves MRI parameter estimation by adopting a noncentral chi noise model in a maximum a posteriori framework, enhancing the physical realism of noise assumptions in quantitative MRI analysis.

Contribution

It extends a maximum a posteriori MRI parameter estimation method by replacing Gaussian noise assumptions with a noncentral chi-distributed noise model, improving accuracy.

Findings

Enhanced accuracy in MRI parameter maps estimation.

Better modeling of noise characteristics in MRI data.

Potential for improved longitudinal MRI studies.

Abstract

Quantitative magnetic resonance imaging (qMRI) allows images to be compared across sites and time points, which is particularly important for assessing long-term conditions or for longitudinal studies. The multiparametric mapping (MPM) protocol is used to acquire images with conventional clinical contrasts, namely PD-, T1-, and MT-weighted volumes. Through multi-echo acquisition for each contrast and variations in flip angles between PD- and T1-weighted contrasts, parameter maps, such as proton density (PD), longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2$^*$), and magnetization transfer saturation (MT$_{sat}$), can be estimated. Various algorithms have been employed to estimate these parameters from the acquired volumes. This paper extends an existing maximum a posteriori approach, which uses joint total variation regularization, by transitioning from a Gaussian noise approximation to a more physically plausible model that assumes noncentral chi-distributed noise.