Matthew D Budde^1
1Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States

Accurate methods of validating diffusion MRI findings are notoriously difficult and lacking. We performed structure tensor (ST) analysis of digital histological images to visualize and quantify brain microstructure on different scales and compare the results to those obtained from DTI. In the normal rat brain, anisotropy derived from the disparate modalities was highly correlated and ST analysis permitted putative crossing fibers to identified and quantified. The method has the potential to accurately validate diffusion MRI findings in the normal and injured or diseased central nervous system.

Leigh A. Johnston^1,2, David Wright^2,3, and Iven M. Mareels^1
1NeuroEngineering Laboratory, Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, Australia, ²Florey Neuroscience Institutes, Parkville, VIC, Australia, ³Centre for Neuroscience, University of Melbourne, Parkville, VIC, Australia

A model of cylindrically restricted diffusion is derived through analytic combination of non-Gaussian phase distribution, applicable to the micron-size axon compartments and diffusion characteristics of mammalian white matter, with an assumed parametric form for the cylinder radii probability density function. The resultant model is presented as an alternative to the AxCaliber method of Assaf et al (2008) that obviates the need for truncation of Bessel functions to solve. We apply the restricted diffusion form in a two-compartment model to two experimental datasets, rat auditory nerve and sheep brain, and demonstrate its ability to infer axon diameter densities.

Alexander Ruh¹, Philipp Emerich¹, Dmitry S. Novikov², and Valerij G. Kiselev^1
1Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany, ²Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States

We show analytically and numerically, that the NMR spectral lineshape can distinguish between different kinds of global structural organization (order and various types of disorder) of tissue susceptibility. This distinction involves long-range correlations and is thereby robust to the biological variability. The biophysical mechanism behind this phenomenon is the molecular diffusion mediating the spin dephasing reflected in the transverse relaxation. Our results provide a framework for novel types of susceptibility-based contrast.

Noam Shemesh¹, and Yoram Cohen^1
1School of Chemistry, Tel Aviv University, Tel Aviv, Israel

Diffusion-diffractions are unique reporters for compartment morphology in porous systems. In single-Pulsed-Field-Gradient (s-PFG) MR the signal decay E(q), is in fact the FT of the averaged propagator, which reports on the compartment size. By contrast, the pore density function, ñ(r), holds all the information on the compartment morphology including size and shape; however, in conventional s-PFG MR, this quantity is intractable. Here, we show that by synergistic application of the diffraction patterns of double-PFG and s-PFG MR, one can in fact obtain the pore density function. Thus, an image of the pore space is obtained, without performing traditional MR imaging.

Carl-Fredrik Westin¹, Markus Nilsson², Ofer Pasternak¹, Daniel Topgaard³, and Hans Knutsson^4
1Department of Radiology, BWH, Harvard Medical School, Boston, MA, United States, ²Department of Medical Radiation Physics, Lund University, Lund, Sweden, ³Division of Physical Chemistry, Lund University, Lund, Sweden, ⁴Department of Biomedical Engineering, Medical Informatics, Linköping University, Linköping, Sweden

Previous experimental results demonstrate that angular double-PFG analysis alleviates the demand for strong gradients for microstructure determination and that estimation of novel features of tissue that displays a microscopic anisotropy may be possible using clinical scanners. We here present new data supporting this claim. The presented work shows that it is possible to perform in vivo double PFG imaging of the human brain with a good SNR, indicating that the new the microstructural contrasts from double-PFG can be made available to studies of clinical populations.

Hui Zhang¹, Torben Schneider², Claudia AM Wheeler-Kingshott², and Daniel C Alexander^1
1Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom, ²NMR Research Unit, Department of Neuroinflammation, UCL Institute of Neurology, University College London, London, United Kingdom

This work proposes neurite orientation dispersion and density imaging (NODDI), a practical diffusion MRI technique for quantifying microstructural complexity of dendrites and axons on clinical scanners. Orientation dispersion and density are two key microstructural features of neurites. Ex vivo studies have shown that estimates of these features from diffusion MRI are consistent with independent histological measures. However, existing protocols are impractical for clinical applications. We address this problem by developing NODDI, a novel neurite imaging and analysis framework with clinical feasibility. We demonstrate that NODDI enables for the first time the whole-brain in vivo imaging of neurite characteristics.

Marco Lawrenz¹, and Jürgen Finsterbusch^1
1Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, Germany

Anisotropy measures derived from DTI reflect a mixture of cell eccentricity and their orientation distribution within the voxel which hampers their informative value. For instance, a vanishing FA in brain white matter is consistent with the absence of eccentric cells like neurons but also with a uniform orientation distribution of neuronal fibers. Here, it is shown that double-wave-vector diffusion-weighting experiments can detect the diffusion anisotropy on a microscopic level in the living human brain. In particular, it is demonstrated that the microscopic anisotropy can also be observed in regions-of-interest that are macroscopically isotropic, i.e. with a FA value of 0.

Els Fieremans¹, Jens H. Jensen², Joseph A. Helpern², Sungheon Kim¹, Robert I. Grossman¹, Matilde Inglese³, and Dmitry S. Novikov^1
1Center for Biomedical Imaging, Radiology, New York University School of Medicine, New York, NY, United States, ²Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, United States, ³Neurology, Mount Sinai School of Medicine, New York, NY, United States

Non-Gaussian diffusion MRI methods provide estimates for the axonal water fraction (AWF) and tortuosity of the extra-axonal space. Using Monte Carlo simulations in a realistic geometry of the corpus callosum, we show that the AWF is most sensitive to axonal loss, whereas the tortuosity is most sensitive to demyelination. The value of this distinction is demonstrated by quantifying the degree of demyelination and axonal loss in multiple sclerosis and in Alzheimer’s disease (AD), where our analysis demonstrates that the change from normal to mild cognitive impairment is mainly explained by demyelination, while conversion into AD is characterized by axonal loss.

Yong Wang¹, Qing Wang¹, Mingqiang Xie², Anne H. Cross², and Sheng-Kwei Song^1
1Radiology, Washington University in St. Louis, Saint Louis, MO, United States, ²Neurology, Washington University in St. Louis, Saint Louis, MO, United States

During CNS pathology, cellularity is enhanced due to increased numbers of microglia, astrocytes and infiltrating inflammatory cells such as macrophages and lymphocytes. Presence of these cells could potentially confound white matter pathology as assessed using DTI. Diffusion basis spectrum imaging (DBSI) has been developed to accurately quantify the extent of cellularity as well as the degree of axonal injury and demyelination in a cuprizone-treated mouse model. In this study, autopsy spinal cord specimens of MS patients were examined to correlate DBSI findings with immunohistochemistry (IHC). Preliminary data indicated that DBSI findings are consistent with IHC results, supporting its clinical application.

Lea Vinokur¹, Daniel Barazany¹, Shimrit Tsur-Moryosef¹, and Yaniv Assaf^1
1Neurobiology, Tel Aviv University, Tel Aviv, Israel

In this study our goal was to measure structural plasticity related changes in white matter induced by spatial learning. Our focus was on the Corpus Callosum of rats that underwent a spatial learning and memory test. We used AxCaliber, an advanced diffusion MRI method to extract morphological parameters of the tissue, such as the Axon Diamter Distribution (ADD) and axonal density. Based on the retrieved parameters the CC's are clustered into 6 clusters using the k-means clustering algorithm. Statistical analysis was performed on each cluster, comparing the learning group to two control groups.