New rapid, accurate T(2) quantification detects pathology in normal-appearing brain regions of relapsing-remitting MS patients

INTRODUCTION: Quantitative T(2) mapping may provide an objective biomarker for occult nervous tissue pathology in relapsing-remitting multiple sclerosis (RRMS). We applied a novel echo modulation curve (EMC) algorithm to identify T(2) changes in normal-appearing brain regions of subjects with RRMS (N = 27) compared to age-matched controls (N = 38). METHODS: The EMC algorithm uses Bloch simulations to model T(2) decay curves in multi-spin-echo MRI sequences, independent of scanner, and scan-settings. T(2) values were extracted from normal-appearing white and gray matter brain regions using both expert manual regions-of-interest and user-independent FreeSurfer segmentation. RESULTS: Compared to conventional exponential T(2) modeling, EMC fitting provided more accurate estimations of T(2) with less variance across scans, MRI systems, and healthy individuals. Thalamic T(2) was increased 8.5% in RRMS subjects (p < 0.001) and could be used to discriminate RRMS from healthy controls well (AUC = 0.913). Manual segmentation detected both statistically significant increases (corpus callosum & temporal stem) and decreases (posterior limb internal capsule) in T(2) associated with RRMS diagnosis (all p < 0.05). In healthy controls, we also observed statistically significant T(2) differences for different white and gray matter structures. CONCLUSIONS: The EMC algorithm precisely characterizes T(2) values, and is able to detect subtle T(2) changes in normal-appearing brain regions of RRMS patients. These presumably capture both axon and myelin changes from inflammation and neurodegeneration. Further, T(2) variations between different brain regions of healthy controls may correlate with distinct nervous tissue environments that differ from one another at a mesoscopic length-scale.