Lower fractional anisotropy at the anterior body of the normal-appearing corpus callosum in multiple sclerosis versus symptomatic carotid occlusion

Neuroradiology. 2009 Sep;51(9):557-61. doi: 10.1007/s00234-009-0535-6. Epub 2009 Jun 6.

Abstract

Introduction: Not uncommonly, differentiating multiple sclerosis (MS) from ischemic cerebral vascular disease is difficult based on conventional magnetic resonance imaging (MRI). We aim to determine whether preferential occult injury in the normal-appearing corpus callosum (NACC) is more severe in patients with MS than symptomatic carotid occlusion by comparing fractional anisotropy (FA) from diffusion tensor imaging (DTI).

Methods: Eighteen patients (eight men, ten women; mean age, 38.6 years) with MS and 32 patients (24 men, eight women; mean age, 64.0 years) with symptomatic unilateral internal carotid occlusion were included. DTI (1.5 T) were performed at corpus callosum which were normal-appearing on fluid-attenuated inversion recovery MRI. Mean FA was obtained from the genu, anterior body, posterior body, and splenium of NACC. Independent-sample t test statistical analysis was performed.

Results: The FA values in various regions of NACC were lower in the MS patients than symptomatic carotid occlusion patients, which was statistically different at the anterior body (0.67 +/- 0.12 vs 0.74 +/- 0.06, P = 0.009), but not at genu, posterior body, and splenium (0.63 +/- 0.09 vs 0.67 +/- 0.07, P = 0.13; 0.68 +/- 0.09 vs 0.73 +/- 0.05, P = 0.07; 0.72 +/- 0.09 vs 0.76 +/- 0.05, P = 0.13).

Conclusion: MS patients have lower FA in the anterior body of NACC compared to patients with symptomatic carotid occlusion. It suggests that DTI has potential ability to differentiate these two conditions due to the more severe preferential occult injury at the anterior body of NACC in MS.

MeSH terms

  • Adult
  • Carotid Stenosis / diagnosis*
  • Corpus Callosum / pathology*
  • Diagnosis, Differential
  • Diffusion Magnetic Resonance Imaging / methods*
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Multiple Sclerosis / diagnosis*
  • Reproducibility of Results
  • Sensitivity and Specificity