Quantitative Assessment of MRI Features in Patients with Relapsing-Remitting Multiple Sclerosis

摘要

Relapsing-remitting multiple sclerosis (RRMS) is a chronic inflammatory demyelinating disease of the central nervous system. Magnetic Resonance Imaging (MRI) has a remarkable role in the assessment of the disease. The aim of the study is to evaluate MRI features and also to determine linear correlation between them. Nineteen RRMS patients (5 males, 14 females) have been included in this study. Neurological examination using EDSS scores was performed for all patients. Each patient underwent Tl, T2, Flair and DWI imaging. Twenty different features were evaluated in the research. Four features obtained directly from images, contain: DWI lesion size, number of DWI lesion, T2 lesion size and number of T2 lesion. In addition, EDSS is considered as one ofthe features. The volume of the lesion is presented as feature of lesion volume which is obtained by suitable software. In this paper we proposed a new method to calculate the rest of MRI features. The method uses histogram analysis of mean parenchymal diffusivity (MPD) map to calculate some features. The obtained features from the method contain: moment 2 to moment 6 (m2-m6), kurtosis, skewness, peak position (PP), peak height (PH), entropy (ENT), MPD-value, white matter fraction (WMF), gray matter fraction (GMF) and brain parenchymal fraction (BPF). Statistical results show EDSS has significant linear correlation with DWI lesion size, number of DWI lesion size, T2 lesion size and PP (P<0.05, R=[0.5-0.6]). All moments, m2-m6, kurtosis, skewness and PHhave significant linear correlation together (P<0.05, R=[0.8-1]). According to the results, ENT has negative significant correlation with m2-m(i. kurtosis, skewness, PH (P<0.05, R=[-0.8-(-0.6)]) but has positive significant correlation with MPD-value and BPF (P<0.05, R=0.5). MPD-value has negative linear correlation with m2-m6, lesion volume and PH (P<0.05, R=[-0.8-(-0.5)]). The proposed method and features could be helpful in the analysis of treatment planning protocol and will be beneficial in the study of treatment effects.