Characterizing Microstructural Tissue Properties in Multiple Sclerosis with Diffusion MRI at 7 T and 3 T: The Impact of the Experimental Design

The recent introduction of advanced magnetic resonance (MR) imaging techniques to characterize focal and global degeneration in multiple sclerosis (MS), like the Composite Hindered and Restricted Model of Diffusion, or CHARMED, diffusional kurtosis imaging (DKI) and Neurite Orientation Dispersion and Density Imaging (NODDI) made available new tools to image axonal pathology non-invasively in vivo. These methods already showed greater sensitivity and specificity compared to conventional diffusion tensor-based metrics (e.g., fractional anisotropy), overcoming some of its limitations. While previous studies uncovered global and focal axonal degeneration in MS patients compared to healthy controls, here our aim is to investigate and compare different diffusion MRI acquisition protocols in their ability to highlight microstructural differences between MS and control tissue over several much used models. For comparison, we contrasted the ability of fractional anisotropy measurements to uncover differences between lesion, normal-appearing white matter (WM), gray matter and healthy tissue under the same imaging protocols. We show that: (1) focal and diffuse differences in several microstructural parameters are observed under clinical settings; (2) advanced models (CHARMED, DKI and NODDI) have increased specificity and sensitivity to neurodegeneration when compared to fractional anisotropy measurements; and (3) both high (3 T) and ultra-high fields (7 T) are viable options for imaging tissue change in MS lesions and normal appearing WM, while higher b-values are less beneficial under the tested short-time (10 min acquisition) conditions.

[1]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[2]  Arthur W. Toga,et al.  Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template , 2008, NeuroImage.

[3]  W. L. Benedict,et al.  Multiple Sclerosis , 2007, Journal - Michigan State Medical Society.

[4]  Derek K. Jones,et al.  Using the biophysical CHARMED model to elucidate the underpinnings of contrast in diffusional kurtosis analysis of diffusion-weighted MRI , 2011, Magnetic Resonance Materials in Physics, Biology and Medicine.

[5]  Rainer Goebel,et al.  Robust and fast nonlinear optimization of diffusion MRI microstructure models , 2017, NeuroImage.

[6]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[7]  W. Teeuwisse,et al.  Simulations of high permittivity materials for 7 T neuroimaging and evaluation of a new barium titanate‐based dielectric , 2012, Magnetic resonance in medicine.

[8]  G. Barker,et al.  Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis , 1999, Neurology.

[9]  Julien Cohen-Adad,et al.  Quality assessment of high angular resolution diffusion imaging data using bootstrap on Q‐ball reconstruction , 2011, Journal of magnetic resonance imaging : JMRI.

[10]  Julien Cohen-Adad,et al.  Improving diffusion MRI using simultaneous multi-slice echo planar imaging , 2012, NeuroImage.

[11]  O. Ciccarelli,et al.  Consensus recommendations for MS cortical lesion scoring using double inversion recovery MRI , 2011, Neurology.

[12]  Tim B. Dyrby,et al.  Orientationally invariant indices of axon diameter and density from diffusion MRI , 2010, NeuroImage.

[13]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. , 1996, Journal of magnetic resonance. Series B.

[14]  J. Helpern,et al.  Diffusional kurtosis imaging: The quantification of non‐gaussian water diffusion by means of magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[15]  Steen Moeller,et al.  Evaluation of slice accelerations using multiband echo planar imaging at 3T , 2013, NeuroImage.

[16]  F. Barkhof,et al.  Axonal loss in multiple sclerosis lesions: Magnetic resonance imaging insights into substrates of disability , 1999, Annals of neurology.

[17]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[18]  Brian Hansen,et al.  Precision and accuracy of diffusion kurtosis estimation and the influence of b‐value selection , 2017, NMR in biomedicine.

[19]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[20]  B. Weinshenker,et al.  Epidemiology of multiple sclerosis. , 1996, Neurologic clinics.

[21]  Julien Cohen-Adad,et al.  In vivo characterization of cortical and white matter neuroaxonal pathology in early multiple sclerosis , 2017, Brain : a journal of neurology.

[22]  B. Murphy,et al.  Signal-to-noise measures for magnetic resonance imagers. , 1993, Magnetic Resonance Imaging.

[23]  Y. Assaf,et al.  Improved precision in CHARMED assessment of white matter through sampling scheme optimization and model parsimony testing , 2014, Magnetic resonance in medicine.

[24]  Jan Sijbers,et al.  ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data , 2009 .

[25]  P. Basser,et al.  New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter , 2004, Magnetic resonance in medicine.

[26]  J. Sijbers,et al.  Diffusion kurtosis imaging probes cortical alterations and white matter pathology following cuprizone induced demyelination and spontaneous remyelination , 2016, NeuroImage.

[27]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[28]  Steen Moeller,et al.  Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRI , 2010, Magnetic resonance in medicine.

[29]  David H. Miller,et al.  Sensitivity of multi-shell NODDI to multiple sclerosis white matter changes: a pilot study. , 2017, Functional neurology.

[30]  F. Barkhof,et al.  Altered diffusion tensor in multiple sclerosis normal‐appearing brain tissue: Cortical diffusion changes seem related to clinical deterioration , 2006, Journal of magnetic resonance imaging : JMRI.

[31]  Thorsten Feiweier,et al.  SAR and scan‐time optimized 3D whole‐brain double inversion recovery imaging at 7T , 2018, Magnetic resonance in medicine.

[32]  M. Horsfield,et al.  Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[33]  E. Hui,et al.  Application of diffusional kurtosis imaging to detect occult brain damage in multiple sclerosis and neuromyelitis optica , 2016, NMR in biomedicine.

[34]  Susan M. Chang,et al.  Clinically feasible NODDI characterization of glioma using multiband EPI at 7 T , 2015, NeuroImage: Clinical.

[35]  R. Grossman,et al.  Quantification of normal-appearing white matter tract integrity in multiple sclerosis: a diffusion kurtosis imaging study , 2016, Journal of Neurology.

[36]  F. Zipp,et al.  Molecular mechanisms linking neuroinflammation and neurodegeneration in MS , 2014, Experimental Neurology.

[37]  Y. Benjamini,et al.  False Discovery Rate–Adjusted Multiple Confidence Intervals for Selected Parameters , 2005 .

[38]  Stamatios N. Sotiropoulos,et al.  An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging , 2016, NeuroImage.

[39]  Nicola Toschi,et al.  Early axonal damage in normal appearing white matter in multiple sclerosis: Novel insights from multi-shell diffusion MRI , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[40]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[41]  Bailey A. Box,et al.  Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients , 2017, NeuroImage: Clinical.

[42]  Bart M. ter Haar Romeny,et al.  Optimal Short-Time Acquisition Schemes in High Angular Resolution Diffusion-Weighted Imaging , 2013, Int. J. Biomed. Imaging.

[43]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[44]  E. Klawiter Current and New Directions in MRI in Multiple Sclerosis , 2013, Continuum.