Joint Annual Meeting ISMRM-ESMRMB 2014 10-16 May 2014 Milan, Italy

Microstructure from all Angles

Tuesday 13 May 2014
Space 1  16:00 - 18:00 Moderators: Daniel C. Alexander, Ph.D., Valerij G. Kiselev, Ph.D.

16:00 0391.   The physical basis of inhomogeneous magnetization transfer
Scott D. Swanson1, David C. Alsop2,3, and Dariya I. Malyarenko1
1Department of Radiology, University of Michigan, Ann Arbor, MI, United States, 2Beth Israel Deaconess Med. Ctr, Boston, MA, United States,3Radiology, Harvard Medical School, Boston, MA, United States

Recent studies have shown that inhomogeneous magnetization transfer (ihMT) can be created in white matter and certain lipid systems by RF saturation at +/- off-resonance frequencies. This work reports on observation of MT and ihMT as a function of temperature in model lipid systems. Our results show that the structure and dynamics of lipids at low temperatures create ihMT which decreases with increasing temperature and is gradually converted into conventional MT by 65 °C. Both MT and ihMT disappear as the sample melts at the 85 °C. A consistent theoretical explanation of ihMT nature is provided in terms of selective dipolar dynamics in model lipid systems.

16:20 0392.   Towards Understanding the Anisotropy of Magnetization Transfer Parameters in Human White Matter
André Pampel1, Henrik Marschner1, Dirk K. Müller1, and Harald E. Möller1
1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

We examine the apparent dependence of qMT parameters, in particular those of the T2 relaxation time of bound protons, on the white matter fiber orientation. It is found that those dependency results from parameter fitting using the Super-Lorentzian lineshape, which renders orientational dependency on other qMT parameters. A novel RF absorption lineshape of the bound pool considering the liquid-crystalline character of the myelin sheath is derived that explains this dependence. Data simulated using this lineshape were analyzed by parameter fitting. A remarkable agreement was found in the obtained parameters compared to those found in WM regions with fractional anisotropy FA>0.7.

16:40 0393.   Combined NODDI and qMT for full-brain g-ratio mapping with complex subvoxel microstructure
Jennifer S.W. Campbell1, Nikola Stikov1, Robert F. Dougherty2, and G. Bruce Pike1,3
1McConnell Brain Imaging Centre, McGill University, Montreal, Quebec, Canada, 2Stanford Center for Neurobiological Imaging, Stanford University, Stanford, California, United States, 3Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada

The myelin g-ratio is a fundamental metric that can be computed from combined diffusion and quantitative magnetization transfer (qMT) imaging. The g-ratio is a function of the fiber volume fraction (FVF) and the myelin volume fraction (MVF). The FVF may be obtained from diffusion tensor imaging, but only in the case where the MRI voxels contain a single fiber system with parallel, straight fibers, making the FA an unsuitable measure of FVF for full-brain quantitative maps. Here, we apply the Neurite Orientation Dispersion and Density Imaging (NODDI) model to obtain full brain g-ratio maps.

17:00 0394.   Quantifying the contribution of white matter microstructure to frequency contrast in gradient echo MRI
Samuel Wharton1 and Richard Bowtell1
1Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom

In most studies involving GE phase imaging, it is assumed that the dominant source of phase contrast is the variation in magnetic susceptibility across different tissues. Recent studies have suggested that white matter microstructure may be an additional source of phase contrast. In this study, we quantify the frequency contribution of white matter microstructure in a small tissue phantom by using sophisticated simulation techniques to model and remove the frequency contribution of bulk isotropic and anisotropic magnetic susceptibility. This approach also yields accurate estimates of the isotropic (-82 ppb) and anisotropic (11 ppb) susceptibility of white matter.

17:20 0395.   A model for extra-axonal diffusion spectra
Wilfred W Lam1, Saad Jbabdi1, and Karla L Miller1
1FMRIB Centre, University of Oxford, Oxford, Oxon, United Kingdom

Diffusion imaging has enormous potential for quantitative measurements of geometric properties that are directly relevant to brain function and pathology. Most work has focused on intra-axonal diffusion, despite the fact that significant signal contrast is expected to arise from the extra-axonal space. White matter diffusion models generally assume no frequency dependence of the extra-axonal diffusion spectrum measured with oscillating gradients. An empirical model is proposed for the diffusion spectra of spins around packed cylinders. Model predictions are compared with Monte Carlo simulations. The model accurately captures salient properties of the entire diffusion spectrum for square, hexagonally, and randomly packed cylinders.

17:40 0396.   
Effects of realistic vascular networks anisotropy on MR microvascular imaging
Nicolas Adrien Pannetier1,2, Thomas Christen3, and Norbert Schuff1,2
1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, 2Centre for Imaging of Neurodegenerative Diseases, VA Medical Center, San Francisco, CA, United States, 3Department of Radiology, Stanford University, CA, United States

MRI microvasculature imaging is a powerful tool for characterizing hemodynamic properties in vivo. However, the structural complexity of the vasculature may introduce inaccuracy in the estimation of the microvasculature. Using full brain vasculature network acquired with optical microscopy and simulation of the MR signal, we characterized the impact of vascular network anisotropy on the estimation of cerebral blood volume (CBV) and vessel size index (VSI). We found an intrinsic orientation dependent variability of about 20% for CBV and VSI. This works indicates that variations in the spatial distributions of vascular networks need to be considered in microvascular MRI for accurate estimations of hemodynamic properties.