Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions

Abstract

Brain blood barrier breakdown as assessed by contrast-enhanced (CE) T1-weighted MR imaging is currently the standard radiological marker of inflammatory activity in multiple sclerosis (MS) patients. Our objective was to evaluate the performance of an alternative model assessing the inflammatory activity of MS lesions by texture analysis of T2-weighted MR images. Twenty-one patients with definite MS were examined on the same 3.0T MR system by T2-weighted, FLAIR, diffusion-weighted and CE-T1 sequences. Lesions and mirrored contralateral areas within the normal appearing white matter (NAWM) were characterized by texture parameters computed from the gray level co-occurrence and run length matrices, and by the apparent diffusion coefficient (ADC). Statistical differences between MS lesions and NAWM were analyzed. ROC analysis and leave-one-out cross-validation were performed to evaluate the performance of individual parameters, and multi-parametric models using linear discriminant analysis (LDA), partial least squares (PLS) and logistic regression (LR) in the identification of CE lesions. ADC and all but one texture parameter were significantly different within white matter lesions compared to within NAWM (p < 0.0167). Using LDA, an 8-texture parameter model identified CE lesions with a sensitivity Se = 70% and a specificity Sp = 76%. Using LR, a 10-texture parameter model performed better with Se = 86% / Sp = 84%. Using PLS, a 6-texture parameter model achieved the highest accuracy with Se = 88% / Sp = 81%. Texture parameter from T2-weighted images can assess brain inflammatory activity with sufficient accuracy to be considered as a potential alternative to enhancement on CE T1-weighted images.

Fig 1. Method of ROI delineation and pixel-wise texture analysis from the gray level co-occurrence matrix (GLCM).

a) Axial-transverse post-contrast T1-W image showing multiple enhanced lesions. b) T2-W image in similar slice location revealing additional hyper-intense unenhanced lesions. c) Segmentation on the same image as in b) of the largest active lesion as well as the contralateral mirrored area within NAWM. d) Corresponding DWI with gradient factor b_o = 0 s.mm^-2. e) Corresponding DWI with gradient factor b = 1000 s.mm^-2. f) ADC parametric map registered on anatomical T2-W image with superimposition of the ROIs drawn on c. g) Zoom of ADC mapped image on largest enhanced lesion (after erasing ROIs’ contours). h-m) Parametrical maps of the following texture parameter: h) contrast, i) correlation, j) homogeneity, k) sum average, l) sum variance and m) difference variance with mean value estimated on a 3x3 sliding window and normalized on the 0–255 range. Individual texture parameters revealed different local and regional statistical properties of the gray levels between MS lesions and NAWM and between enhanced and unenhanced MS lesions.

Fig 2. Receiver-Operating Characteristic analysis for evaluating the performance of individual parameters and multiparametric models in discriminating enhanced lesions from unhencanced lesions.