Neurite orientation dispersion and density imaging of the healthy cervical spinal cord in vivo

doi:10.1016/j.neuroimage.2015.01.045

NeuroImage

Volume 111, 1 May 2015, Pages 590-601

https://doi.org/10.1016/j.neuroimage.2015.01.045 Get rights and content

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Highlights

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We present the first in vivo application of NODDI to the healthy cervical spinal cord.
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We acquired multi-shell diffusion MRI data from five healthy volunteers at 3 T and performed NODDI and DTI analysis.
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NODDI outperforms DTI in terms of quality of fit and disentangles key factors contributing to diffusion anisotropy.
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The reproducibility of NODDI allows its application in future studies of the spinal cord.
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We conclude that NODDI is a feasible alternative to DTI for in vivo spinal cord diffusion imaging.

Abstract

Here we present the application of neurite orientation dispersion and density imaging (NODDI) to the healthy spinal cord in vivo. NODDI provides maps such as the intra-neurite tissue volume fraction (v_in), the orientation dispersion index (ODI) and the isotropic volume fraction (v_iso), and here we investigate their potential for spinal cord imaging. We scanned five healthy volunteers, four of whom twice, on a 3 T MRI system with a ZOOM-EPI sequence. In accordance to the published NODDI protocol, multiple b-shells were acquired at cervical level and both NODDI and diffusion tensor imaging (DTI) metrics were obtained and analysed to: i) characterise differences in grey and white matter (GM/WM); ii) assess the scan–rescan reproducibility of NODDI; iii) investigate the relationship between NODDI and DTI; and iv) compare the quality of fit of NODDI and DTI. Our results demonstrated that: i) anatomical features can be identified in NODDI maps, such as clear contrast between GM and WM in ODI; ii) the variabilities of v_in and ODI are comparable to that of DTI and are driven by biological differences between subjects for ODI, have similar contribution from measurement errors and biological variation for v_in, whereas v_iso shows higher variability, driven by measurement errors; iii) NODDI identifies potential sources contributing to DTI indices, as in the brain; and iv) NODDI outperforms DTI in terms of quality of fit. In conclusion, this work shows that NODDI is a useful model for in vivo diffusion MRI of the spinal cord, providing metrics closely related to tissue microstructure, in line with findings in the brain.