Perfusion MRI in automatic classification of multiple sclerosis lesion subtypes - Ehsan Homayouny; Rasoul Mahdavifar Khayati; Seyed Massood Nabavi; Vania Karami (IET Signal Processing)
Abstract This retrospective and exploratory study investigated the efficiency of the 3T perfusion magnetic resonance imaging (MRI) at the classification of MS lesion subtypes. For the MS lesion subtype classification, firstly, it was necessary to segment all MS lesions. Therefore, a Bayesian classifier based on the adaptive mixture method was used to segment all lesions, and an artificial neural network (ANN) employed a multi‐layer Perceptron as a subtype classifier. The Bayesian classifier accomplished the segmentation of lesions using Fluid Attenuated Inversion Recovery automatically, and the ANN part was used as a subtype classifier that worked based on extracted information from perfusion MRI (i.e. Mean Transit Time and Cerebral Blood Volume maps) along with the intensity information of the conventional multi‐channel MRI in segmented lesions. Adding 3‐Tesla perfusion MRI to the proposed model for the subtype classification led to an increment of about 7% and 13% in the sensitivity of acute and chronic lesion classifications, respectively. The sensitivity of T2 lesions did not meaningfully change. The overall accuracy of the classification for acute, chronic, and T2 lesion classifications was 96.1%, 90.5%, and 92.9%, respectively. The proposed architectures reached high sensitivity in discrimination between MS lesion subtypes when 3T perfusion MRIs were used.