Evolution of MRI Parameters from the Subacute to Chronic Phase After Human Traumatic Cervical Spinal Cord Injury:

Andreas Grillhösl1, Iris Leister2, Florian Högel3

  • 1Department of Radiology, Neuroradiology and Interventional Radiology, BG Trauma Center Murnau, Murnau, Germany.

Clinical Neuroradiology
|October 24, 2025
PubMed
Abstract

Insights

Diffusion tensor imaging (DTI) reveals ongoing spinal cord degeneration up to one year after injury. These longitudinal DTI metrics track microstructural changes, aiding in prognosis and treatment monitoring for spinal cord injury (SCI).

Area of Science:

  • Neuroimaging
  • Spinal Cord Injury Research
  • Diffusion Tensor Imaging

Background:

  • Magnetic resonance imaging (MRI) is standard for assessing spinal cord injury (SCI) lesions.
  • Diffusion tensor imaging (DTI) provides microstructural insights into white matter changes.
  • Few studies have longitudinally tracked SCI changes from acute to chronic stages.

Purpose of the Study:

  • To analyze the evolution of DTI metrics over the first year following cervical SCI.
  • To investigate longitudinal microstructural changes during the acute-to-chronic transition in SCI.
  • To assess DTI's potential for monitoring SCI pathology over time.

Main Methods:

  • Prospective longitudinal study of 52 traumatic cervical SCI patients.
  • MRI and neurological assessments (ISNCSCI) at 1 month, 3 months, and 1 year post-injury.
  • Linear mixed model analyses to evaluate DTI metrics (FA, MD, AD) over time.

Main Results:

  • Fractional anisotropy (FA) decreased longitudinally, indicating ongoing degeneration.
  • Mean diffusivity (MD) increased significantly over the year, suggesting progressive microstructural changes.
  • Axial diffusivity (AD) showed dynamic changes, decreasing then returning to baseline values.

Conclusions:

  • DTI is valuable for monitoring microstructural changes post-SCI.
  • Longitudinal imaging provides insights into SCI pathology evolution.
  • DTI aids in prognostic modeling, treatment monitoring, and outcome prediction for SCI.

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