Multi-compartment diffusion MRI, T2 relaxometry and myelin water imaging as neuroimaging descriptors for anomalous tissue detection

TL;DR

This study combines advanced MRI techniques with machine learning to improve detection of abnormal tissue in multiple sclerosis, surpassing traditional methods in specificity and sensitivity.

Contribution

It introduces a novel multi-modal MRI feature set combined with a boosting decision-tree classifier for better tissue abnormality detection in MS.

Findings

Enhanced classification accuracy of abnormal tissue.

Multi-modal MRI features outperform single-modality approaches.

Effective integration of diffusion MRI and T2 relaxometry data.

Abstract

Multiple sclerosis (MS) is an inflammatory and neurodegenerative disease characterized by diffuse and focal areas of tissue loss. Conventional MRI techniques such as T1-weighted and T2-weighted scans are generally used in the diagnosis and prognosis of the disease. Yet, these methods are limited by the lack of specificity between lesions, their perilesional area and non-lesional tissue. Alternative MRI techniques exhibit a higher level of sensitivity to focal and diffuse MS pathology than conventional MRI acquisitions. However, they still suffer from limited specificity when considered alone. In this work, we have combined tissue microstructure information derived from multicompartment diffusion MRI and T2 relaxometry models to explore the voxel-based prediction power of a machine learning model in a cohort of MS patients and healthy controls. Our results show that the combination of multi-modal features, together with a boosting enhanced decision-tree based classifier, which combines a set of weak classifiers to form a strong classifier via a voting mechanism, is able to utilise the complementary information for the classification of abnormal tissue.