New rapid, accurate T₂ quantification detects pathology in normal-appearing brain regions of relapsing-remitting MS patients

Introduction: Quantitative T₂ 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 T₂ 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 T₂ decay curves in multi-spin-echo MRI sequences, independent of scanner, and scan-settings. T₂ 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 T₂ modeling, EMC fitting provided more accurate estimations of T₂ with less variance across scans, MRI systems, and healthy individuals. Thalamic T₂ 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 T₂ associated with RRMS diagnosis (all p < 0.05). In healthy controls, we also observed statistically significant T₂ differences for different white and gray matter structures.

Conclusions: The EMC algorithm precisely characterizes T₂ values, and is able to detect subtle T₂ changes in normal-appearing brain regions of RRMS patients. These presumably capture both axon and myelin changes from inflammation and neurodegeneration. Further, T₂ 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.