Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

INTRODUCTION: Diffusion tensor imaging (DTI) reveals white matter pathology in patients with multiple sclerosis (MS). A recent non-Gaussian diffusion imaging technique, q-space imaging (QSI), may provide several advantages over conventional MRI techniques in regard to in vivo evaluation of the disease process in patients with MS. The purpose of this study is to investigate the use of root mean square displacement (RMSD) derived from QSI data to characterize plaques, periplaque white matter (PWM), and normal-appearing white matter (NAWM) in patients with MS. METHODS: We generated apparent diffusion coefficient (ADC) and fractional anisotropy (FA) maps by using conventional DTI data from 21 MS patients; we generated RMSD maps by using QSI data from these patients. We used the Steel-Dwass test to compare the diffusion metrics of regions of interest in plaques, PWM, and NAWM. RESULTS: ADC differed (P<0.05) between plaques and PWM and between plaques and NAWM. FA differed (P<0.05) between plaques and NAWM. RMSD differed (P<0.05) between plaques and PWM, plaques and NAWM, and PWM and NAWM. CONCLUSION: RMSD values from QSI may reflect microstructural changes and white-matter damage in patients with MS with higher sensitivity than do conventional ADC and FA values.

Original publication




Journal article


Magn Reson Imaging

Publication Date





625 - 629


Diffusion tensor imaging, Multiple sclerosis, Normal-appearing white matter, Periplaque white matter, Root mean square displacement, q-space imaging, Adult, Anisotropy, Diffusion Tensor Imaging, Female, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Multiple Sclerosis, Reproducibility of Results, White Matter