Hong-Hsi Lee¹, Jelle Veraart^1,2, Dmitry S. Novikov¹, and Els Fieremans^1
1New York University, Center for Biomedical Imaging, New York, NY, United States, ²iMinds-Vision Lab, University of Antwerp, Antwerp, Belgium

Diffusion MRI enables to evaluate microstructure at the mesoscopic scale. In particular, tuning the diffusion time over a wide range could increase the sensitivity for acquiring useful biomarkers, such as the axonal diameter or density. However, it is unclear whether either intra-, or extra-axonal water attribute most to the observed changes of diffusion signal with diffusion time. Here, we evaluate the dependence of the diffusion coefficient (obtained from the diffusion signal at low $$$b$$$-value) on $$$\delta$$$ and $$$\Delta$$$ in the direction perpendicular to axons in the human brain, and explain these dependencies by diffusion of water in the extra-axonal space.

Hua Li¹, Xiaoyu Jiang¹, Jingping Xie¹, John C Gore¹, and Junzhong Xu^1
1Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, United States

Current diffusion MRI methods that quantitatively characterize tissue microstructure usually assume a zero transcytolemmal water exchange rate $$$\tau_{in}$$$ between intra- and extracellular spaces. This assumption may not be true in many cases of interest. The present work used both computer simulations and cell culture in vitro to investigate the influence of $$$\tau_{in}$$$ on the accuracy of fitted microstructural parameters such as mean cell size $$$d$$$, intracellular water fraction $$$f_{in}$$$ and diffusion coefficient $$$D_{in}$$$. Results indicate $$$d$$$ is relatively insensitive to $$$\tau_{in}$$$, while $$$f_{in}$$$ is always underestimated with finite $$$\tau_{in}$$$. $$$D_{in}$$$ can be fit reliably only when short diffusion times are used.  

Lebina Shrestha Kakkar¹, Oscar Bennett¹, David Atkinson², Bernard Siow³, James Phillips⁴, Simon Richardson³, Enrico Kaden¹, and Ivana Drobnjak^1
1Centre for Medical Image Computing, University College London, London, United Kingdom, ²Centre for Medical Imaging, University College London, London, United Kingdom, ³Centre for Advanced Biomedical Imaging, University College London, London, United Kingdom, ⁴Department of Biomaterials & Tissue Engineering, University College London, London, United Kingdom

In a recent simulation study, Drobnjak et al demonstrates that low-frequency oscillating gradient spin-echo (OGSE) sequence is more sensitive to axon diameter than conventional pulsed gradient spin-echo (PGSE) sequence when fibre orientation is unknown or when fibre dispersion exists. Here, we experimentally validate this claim. We image a live rat sciatic nerve tissue using both sequences and compare its agreement with histology. Our results confirm that OGSE provides more accurate and precise diameter estimates compared to PGSE. Additionally, OGSE parameter estimates are less affected by reduced number of diffusion gradient directions, suggesting their use could translate into faster scan times.

Achille Teillac^1,2,3, Sandrine Lefranc^2,3,4, Edouard Duchesnay^2,3,4,5, Fabrice Poupon^2,3,4, Maite Alaitz Ripoll Fuster^1,2,3, Denis Le Bihan^1,2,3, Jean François Mangin^2,3,4,5, and Cyril Poupon^1,2,3,5
1CEA NeuroSpin / UNIRS, Gif-sur-Yvette, France, ²Université Paris-Saclay, Orsay, France, ³France Life Imaging, Orsay, France, ⁴CEA NeuroSpin / UNATI, Gif-sur-Yvette, France, ⁵http://cati-neuroimaging.com/, Gif-sur-Yvette, France

In this study, we investigated the dendrite density in cortical areas with diffusion MR microscopy using the NODDI model and showed, on a population of healthy volunteers, significant differences between left and right hemisphere, correlated with their supported brain functions.

Daeun Kim¹, Joong Hee Kim², and Justin P Haldar^1
1Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States, ²Department of Neurology, Washington University, St. Louis, MO, United States

We propose a new MR experiment called diffusion-relaxation correlation spectroscopic imaging (DR-CSI).  DR-CSI acquires imaging data across a range of different b-value and echo time combinations, and then performs regularized reconstruction to generate a 2D diffusion-relaxation correlation spectrum for every voxel.  The peaks of this spectrum correspond to the different tissue microenvironments that are present within each macroscopic imaging voxel, which provides powerful insight into the tissue microstructure. Compared to standard relaxometry or diffusion imaging, DR-CSI provides unique capabilities to resolve tissue compartments that have similar relaxation or diffusion parameters.  DR-CSI is demonstrated with spinal cord traumatic injury MRI data.

Barbara Wichtmann^1,2, Susie Huang¹, Qiuyun Fan¹, Thomas Witzel¹, Elizabeth Gerstner³, Bruce Rosen¹, Lothar Schad², Lawrence Wald^1,4, and Aapo Nummenmaa^1
1A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States, ²Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, ³Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, ⁴Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

We propose a new analysis technique called Linear Multi-scale Modeling (LMM) for diffusion MRI data that enables detailed microstructural tissue characterization by separating orientation distributions of restricted and hindered diffusion water compartments over a range of length scales. We demonstrate the ability of LMM to estimate volume fractions, compartment sizes and orientation distributions utilizing both simulations as well as empirical data from one healthy subject and one tumor patient acquired using a human 3T MRI scanner equipped with a 300mT/m gradient system. Possible applications of our modeling framework include characterization of diffusion microstructural signatures of pathological vs. healthy tissue.

Inbar Seroussi¹, Ofer Pasternak^1,2, and Nir Sochen ^1
1Tel-Aviv university, Tel Aviv, Israel, ²Psychiatry and Radiology, Harvard Medical School, Boston, MA, United States

How to bridge microscopic molecular motion with macroscopic diffusion MR signal? We suggest a simple stochastic microscopic model for molecular motion within a magnetic field. We derive the Fokker-Planck equation of this model, which is an analytic expression of the probability density function describing the magnetic diffusion propagator. This propagator is a crucial quantity and provides the link between the microscopic equations and the measured MR signal. Using the propagator we derive a generalized version for the macroscopic Bloch-Torrey equation. The advantage of this derivation is that it does not require assumptions such as constant diffusion coefficient, or ad-hoc selection of a propagator. In fact, we show that the generalized Bloch-Torrey equations have an additional term that was previously neglected and accounts for spatial varying diffusion coefficient. Including this term better predicts MR signal in complex microstructures, such as those expected in most biological experiments.

Markus Nilsson¹, Samo Lasic², Daniel Topgaard³, and Carl-Fredrik Westin^4
1Lund University Bioimaging Center, Lund University, Lund, Sweden, ²CR Development AB, Lund, Sweden, ³Physical Chemistry, Lund University, Lund, Sweden, ⁴Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States

The use of non-conventional gradient waveforms has brought renewed interest in non-invasive estimation of the axon diameter from diffusion MRI. Using conventional single diffusion encoding (SDE) at clinical MRI scanners, axons smaller than the resolution limit (4-5 microns) were indistinguishable from each other. We here predict the resolution limit for arbitrary gradient waveforms in systems with (i) parallel axons and (ii) orientation dispersion. Results show that SDE is optimal for parallel fibers, but that multiple diffusion encodings (MDE) are preferred where there is orientation dispersion. With 300 mT/m and MDE, the resolution limit was 2.8 microns for dispersed fibers.

Raimo A. Salo¹, Ilya Belevich², Eppu Manninen¹, Eija Jokitalo², Olli Gröhn¹, and Alejandra Sierra^1
1Department of Neurobiology, A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland, ²Institute of Biotechnology, University of Helsinki, Helsinki, Finland

Diffusion tensor imaging (DTI) is a widely used tool, however, the contribution of brain tissue microstructure into DTI contrast is not fully understood. In this work, we propose using serial block-face scanning electron microscopy (SBEM) and Fourier analysis to gain insight into this contribution. We calculated anisotropy and orientation from SBEM stacks and compare the values to fractional anisotropy and orientation from in vivo and ex vivo DTI. This work will give new insights to the contribution of microstructure to DTI contrast in normal brain and during pathology.

Marco Palombo^1,2, Clémence Ligneul^1,2, Chloé Najac^1,2, Juliette Le Douce^1,2, Julien Flament^1,2, Carole Escartin^1,2, Philippe Hantraye^1,2, Emmanuel Brouillet^1,2, Gilles Bonvento^1,2, and Julien Valette^1,2
1CEA/DSV/I2BM/MIRCen, Fontenay-aux-Roses, France, ²CNRS Université Paris-Saclay UMR 9199, Fontenay-aux-Roses, France

We introduce a novel paradigm for non-invasive brain microstructure quantification, where original diffusion modeling is merged with cutting-edge diffusion-weighted spectroscopy (DW-MRS) experiments to capture features of cellular morphology that have remained largely ignored by DW-MRI. A compact description of long-range cellular morphology is used to randomly generate large collections of synthetic cells where particles diffusion is simulated. After investigating model robustness, we apply it on metabolite ADC measured in vivo in the monkey brain up to t_d=2 seconds. The new paradigm introduced here opens new possibilities to non-invasively extract quantitative information about cell size, complexity and heterogeneity in the brain.