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Updated: Jul 10, 2026

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
Published on: May 7, 2019
Standard Model Imaging in the Brain and Spinal Cord of MS Patients: Initial Assessment and Comparison to Diffusion
Atlee Witt1,2, Alicia E Cronin1,3, Bailey Busher1
1Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, USA.
None:
Multiple sclerosis (MS) pathology manifests across the central nervous system (CNS), yet magnetic resonance imaging (MRI) studies frequently assess the brain and spinal cord (SC) in isolation. It remains unclear whether microstructural injury in normal-appearing white matter (NAWM) and lesions manifests identically across these distinct anatomical environments or if advanced biophysical models offer diagnostic advantages over standard diffusion techniques. We performed a comprehensive 3T MRI protocol with same-session brain and cervical SC imaging on 34 persons with relapsing-remitting MS (pwRRMS) and 36 healthy controls (HCs). We focused on diffusion frameworks (diffusion tensor imaging [DTI], standard model imaging with free water [SMIfw]) to quantify and compare microstructural integrity between the brain and cervical SC for the first time. Specifically, we assessed the feasibility of SMI for clinical implementation in terms of sensitivity (contrast between lesions and NAWM in pwRRMS) and reliability (HC reproducibility). Our findings indicate that both simple (DTI) and advanced (SMI) models are sensitive to MS-related microstructural changes, but the utility varied by region: notably, SMI-derived neurite density fraction (f) emerged as a robust marker of MS pathology in both the brain and SC, while DTI metrics like fractional anisotropy (FA) and radial diffusivity (RD) showed comparable sensitivity exclusively in the brain. Though axonal loss appears to be a global feature detectable even in early-stage disease, the inflammatory or edematous environment of lesions differs fundamentally between the brain and SC. Taken together, these findings demonstrate the utility of SMI for comprehensive CNS assessment and support its potential for future usage in clinical trials and improved disease monitoring.
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