An in vivo study of the orientation‐dependent and independent components of transverse relaxation rates in white matter

Diffusion‐weighted imaging (DWI) provides information that allows the estimation of white‐matter (WM) fibre orientation and distribution, but it does not provide information about myelin density, fibre concentration or fibre size within each voxel. On the other hand, quantitative relaxation contrasts (like the apparent transverse relaxation, R2∗ ) offer iron and myelin‐related contrast, but their dependence on the orientation of microstructure with respect to the applied magnetic field, B0, is often neglected. The aim of this work was to combine the fibre orientation information retrieved from the DWI acquisition and the sensitivity to microstructural information from quantitative relaxation parameters. The in vivo measured quantitative transverse relaxation maps (R2 and R2∗ ) were decomposed into their orientation‐dependent and independent components, using the DWI fibre orientation information as prior knowledge.

[1]  R. Bowtell,et al.  Fiber orientation-dependent white matter contrast in gradient echo MRI , 2012, Proceedings of the National Academy of Sciences.

[2]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[3]  Marcel P. Zwiers,et al.  Patching cardiac and head motion artefacts in diffusion-weighted images , 2010, NeuroImage.

[4]  Julien Cohen-Adad,et al.  In vivo histology of the myelin g-ratio with magnetic resonance imaging , 2015, NeuroImage.

[5]  Jeff H. Duyn,et al.  T2*-based fiber orientation mapping , 2011, NeuroImage.

[6]  Thomas R. Knösche,et al.  White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI , 2013, NeuroImage.

[7]  Richard Bowtell,et al.  Gradient echo based fiber orientation mapping using R2* and frequency difference measurements , 2013, NeuroImage.

[8]  M. Bronskill,et al.  Anisotropy of NMR properties of tissues , 1994, Magnetic resonance in medicine.

[9]  Zang-Hee Cho,et al.  Origin of B0 orientation dependent R2 * (=1/T2 *) in white matter , 2013, NeuroImage.

[10]  Jens Frahm,et al.  Fast T2 Mapping With Improved Accuracy Using Undersampled Spin-Echo MRI and Model-Based Reconstructions With a Generating Function , 2014, IEEE Transactions on Medical Imaging.

[11]  D. Louis Collins,et al.  Diffusion Weighted Image Denoising Using Overcomplete Local PCA , 2013, PloS one.

[12]  Richard Bowtell,et al.  T2* measurements in human brain at 1.5, 3 and 7 T. , 2007, Magnetic resonance imaging.

[13]  Se-Hong Oh,et al.  Origin of B 0 orientation dependent R 2 * ( = 1 / T 2 * ) in white matter : magic angle effect vs . magnetic susceptibility , 2012 .

[14]  Risto A. Kauppinen,et al.  Diffusion-mediated nuclear spin phase decoherence in cylindrically porous materials , 2016, Journal of magnetic resonance.

[15]  M. P. Zwiers,et al.  EPI DISTORTION CORRECTION BY CONSTRAINED NONLINEAR COREGISTRATION IMPROVES GROUP FMRI , 2009 .

[16]  David H. Miller,et al.  Imaging outcomes for neuroprotection and repair in multiple sclerosis trials , 2009, Nature Reviews Neurology.

[17]  Dmitriy A Yablonskiy,et al.  Voxel spread function method for correction of magnetic field inhomogeneity effects in quantitative gradient‐echo‐based MRI , 2013, Magnetic resonance in medicine.

[18]  R. S. Hinks,et al.  Real‐time shimming to compensate for respiration‐induced B0 fluctuations , 2007, Magnetic resonance in medicine.

[19]  J. Duyn,et al.  Characterization of T2* heterogeneity in human brain white matter , 2009, Magnetic resonance in medicine.

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

[21]  G. Pike,et al.  Correction for B1 and B0 variations in quantitative T2 measurements using MRI , 2000, Magnetic resonance in medicine.

[22]  Yaniv Assaf,et al.  Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain , 2005, NeuroImage.

[23]  Rolf Gruetter,et al.  In vivo assessment of myelination by phase imaging at high magnetic field , 2012, NeuroImage.

[24]  Christian Langkammer,et al.  Differential developmental trajectories of magnetic susceptibility in human brain gray and white matter over the lifespan , 2014, Human brain mapping.

[25]  Jean-Philippe Thiran,et al.  MARTINI and GRAPPA - When Speed is Taste , 2014 .

[26]  G. Bartzokis,et al.  Multimodal Magnetic Resonance Imaging Assessment of White Matter Aging Trajectories Over the Lifespan of Healthy Individuals , 2012, Biological Psychiatry.

[27]  D. Le Bihan,et al.  Orientation Dependence of White Matter T 2 * Contrast at 7 T : A Direct Demonstration , 2007 .

[28]  Carlo Caltagirone,et al.  Characterization of white matter fiber bundles with T  2* relaxometry and diffusion tensor imaging , 2009, Magnetic resonance in medicine.

[29]  G. Bartzokis Alzheimer's disease as homeostatic responses to age-related myelin breakdown , 2011, Neurobiology of Aging.

[30]  U. Klose,et al.  The in vivo influence of white matter fiber orientation towards B0 on T2* in the human brain , 2010, NMR in biomedicine.

[31]  Dmitriy A Yablonskiy,et al.  On the role of physiological fluctuations in quantitative gradient echo MRI: implications for GEPCI, QSM, and SWI , 2015, Magnetic resonance in medicine.

[32]  Tobias Kober,et al.  MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field , 2010, NeuroImage.

[33]  Jens Frahm,et al.  Model‐based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin‐echo MRI , 2011, Journal of magnetic resonance imaging : JMRI.

[34]  B. Wandell,et al.  Lifespan maturation and degeneration of human brain white matter , 2014, Nature Communications.

[35]  Hui Zhang,et al.  Assessing white matter microstructure of the newborn with multi-shell diffusion MRI and biophysical compartment models , 2014, NeuroImage.

[36]  Hao Huang,et al.  White matter cerebral blood flow is inversely correlated with structural and functional connectivity in the human brain , 2011, NeuroImage.

[37]  Michael Lustig,et al.  Coil compression for accelerated imaging with Cartesian sampling , 2013, Magnetic resonance in medicine.

[38]  Oliver Speck,et al.  Prospective motion correction in brain imaging: A review , 2013, Magnetic resonance in medicine.

[39]  Jeff H Duyn,et al.  A torque balance measurement of anisotropy of the magnetic susceptibility in white matter , 2015, Magnetic resonance in medicine.

[40]  M. Fukunaga,et al.  Sensitivity of MRI resonance frequency to the orientation of brain tissue microstructure , 2010, Proceedings of the National Academy of Sciences.

[41]  Society of magnetic resonance in medicine , 1990 .

[42]  Thomas H. B. FitzGerald,et al.  Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging , 2014, Neurobiology of Aging.

[43]  G. Fullerton,et al.  Orientation of tendons in the magnetic field and its effect on T2 relaxation times. , 1985, Radiology.

[44]  Rolf Gruetter,et al.  A modulated closed form solution for quantitative susceptibility mapping — A thorough evaluation and comparison to iterative methods based on edge prior knowledge , 2015, NeuroImage.

[45]  R. Gruetter,et al.  Retrospective correction of involuntary microscopic head movement using highly accelerated fat image navigators (3D FatNavs) at 7T , 2016, Magnetic resonance in medicine.

[46]  a.R.V.,et al.  Human Neuroanatomy , 1954, Neurology.

[47]  Jeff H. Duyn,et al.  Micro-compartment specific T2 ⁎ relaxation in the brain , 2013, NeuroImage.

[48]  Michael J. Knight,et al.  Anisotropy of spin-echo T2 relaxation by magnetic resonance imaging in the human brain in vivo , 2015 .

[49]  José P Marques,et al.  Using forward calculations of the magnetic field perturbation due to a realistic vascular model to explore the BOLD effect , 2008, NMR in biomedicine.