  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.
