Multi-compartmental diffusion characterization of the human cervical spinal cord in vivo using the spherical mean technique

NMR Biomed. 2018 Apr;31(4):e3894. doi: 10.1002/nbm.3894. Epub 2018 Feb 1.

Abstract

The purpose of this work was to evaluate the feasibility and reproducibility of the spherical mean technique (SMT), a multi-compartmental diffusion model, in the spinal cord of healthy controls, and to assess its ability to improve spinal cord characterization in multiple sclerosis (MS) patients at 3 T. SMT was applied in the cervical spinal cord of eight controls and six relapsing-remitting MS patients. SMT provides an elegant framework to model the apparent axonal volume fraction vax , intrinsic diffusivity Dax , and extra-axonal transverse diffusivity Dex_perp (which is estimated as a function of vax and Dax ) without confounds related to complex fiber orientation distribution that reside in diffusion MRI modeling. SMT's reproducibility was assessed with two different scans within a month, and SMT-derived indices in healthy and MS cohorts were compared. The influence of acquisition scheme on SMT was also evaluated. SMT's vax , Dax , and Dex_perp measurements all showed high reproducibility. A decrease in vax was observed at the site of lesions and normal appearing white matter (p < 0.05), and trends towards a decreased Dax and increased Dex_perp were seen. Importantly, a twofold reduction in acquisition yielded similarly high accuracy with SMT. SMT provides a fast, reproducible, and accurate method to improve characterization of the cervical spinal cord, and may have clinical potential for MS patients.

Keywords: axon; diffusion; multiple sclerosis; spherical mean technique; spinal cord; volume fraction.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Cervical Cord / diagnostic imaging*
  • Cohort Studies
  • Diffusion Magnetic Resonance Imaging*
  • Humans
  • Multiple Sclerosis / diagnostic imaging
  • Reproducibility of Results