Mariam Andersson^1,2, Jonathan Rafael-Patino³, Hans Martin Kjer^1,2, Vedrana Andersen Dahl¹, Alexandra Pacureanu⁴, Martin Bech⁵, Anders Bjorholm Dahl¹, Jean-Philippe Thiran^3,6, and Tim B. Dyrby^1,2
1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark, ²Danish Research Centre for Magnetic Resonance, Hvidovre, Denmark, ³Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁴The European Synchrotron, Grenoble, France, ⁵Department of Medical Radiation Physics, Clinical Science, Lund University, Lund, Sweden, ⁶Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland

We extract the orientation dispersion (OD) and 3D axon diameter variations of long axons (>100 µm) from an ultra-high resolution, synchrotron X-ray nano-holotomography (XNH) scan of the monkey splenium. From this, we discover a relationship between mean axon diameter and along-axon diameter variations. Monte Carlo simulations are then performed on the intra-axonal spaces (IAS) of different substrates which inherit their morphological features from the segmented axons. These simulations show that the OD significantly affects the transverse apparent diffusion coefficient (ADC_⟂) of the axon substrate at diffusion times up to 50 ms, while diameter variations do not.

Alexandru V Avram^1,2, Kadharbatcha Saleem^1,2, Frank Q Ye³, Cecil Yen⁴, Michal E Komlosh^1,2, and Peter J Basser^1
1NICHD, National Institutes of Health, Bethesda, MD, United States, ²The Henry Jackson Foundation, Bethesda, MD, United States, ³NIMH, National Institutes of Health, Bethesda, MD, United States, ⁴NINDS, National Institutes of Health, Bethesda, MD, United States

We quantified the alignment between the DTI reference frame (DRF) and the cortical reference frame (CRF) throughout the entire cerebral cortex in a macaque brain, and found relatively good correspondence, especially in regions with high curvature such as the gyral walls and the cortical sulci. Based on this correspondence, we analyze cortical diffusion signals in the CRF and construct a simple model of cortical diffusion with distinct radial (columnar) and tangential (sheet-like) diffusion processes in cortical layers. The variation of model parameters with cortical depth reflects architectonic features described in a histologically defined digital macaque brain atlas.

Hong-Hsi Lee¹, Antonios Papaioannou¹, Dmitry S Novikov¹, and Els Fieremans^1
1Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, New York, NY, United States

The purpose of this work is to identify the relevant microstructural features for the human brain gray matter. For that, we estimate the diffusivity and kurtosis time-dependence in 25 gray matter sub-regions of 10 healthy subjects, and compare the effects of the structural disorder and exchange (Karger model). The estimated power-law dynamical exponent θ≈1/2 is consistent with the structural disorder picture in a 1-dimensional micro-geometry of randomly positioned restrictions along neurites. In contrast, Karger model yields exchange time much shorter than values in previous studies, and below our shortest diffusion time.

Ileana Ozana Jelescu¹ and Dmitry S Novikov^2
1Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ²Dept. of Radiology, New York University School of Medicine, New York, NY, United States

In the absence of a myelin sheath, exchange generally is non-negligible over the typical diffusion times of MRI experiments (10 – 100 ms) and should be accounted for in gray matter modeling. Here we use time-dependent kurtosis and the Kärger model (KM) of two slowly exchanging compartments to evaluate water exchange time between intra-neurite and extra-cellular compartments in rat GM in vivo. We report exchange times on the order of 10 – 30 ms. Future work will focus on exploring a broader range of diffusion times to test the asymptotic decay of kurtosis toward zero.

Jonas Lynge Olesen^1,2 and Sune Nørhøj Jespersen^1,2
1Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, ²Department of physics and Astronomy, Aarhus University, Aarhus, Denmark

A main idea contained in the standard model of diffusion is to model neurons with zero-width sticks. A resulting signature is the prediction that in the large b limit, the isotropically averaged signal scales as $$$1/\sqrt{b}$$$ which has been verified in white matter but not gray matter. This has multiple proposed causes including dendrite curvature and branching. Here, we report on Monte Carlo simulations in 3D reconstructed neurons and find that branching and curvature do not break the power law scaling. On the other hand,the soma is found to limit the regime in which stick scaling is observed.

Rafael Neto Henriques¹, Sune Nørhøj Jespersen^2,3, and Noam Shemesh^1
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, ²Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Clinical Institute, Aarhus University, Aarhus, Denmark, ³Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark

Resolving the different sources of diffusional kurtosis can increase DKI’s specificity towards different microstructural features. Such sources can be resolved using the correlation tensor imaging (CTI) – a novel double diffusion encoding technique that does not rely on common assumptions of time-independent diffusion. Here, a minimal acquisition protocol for CTI is designed and used to characterize the diffusional kurtosis of living rat brains for the first time. We here develop an approach to acquire CTI data in vivo and show that it can robustly decouple inter-compartmental kurtosis sources (anisotropic and isotropic diffusivity variances) from intra-compartmental kurtosis sources.

Markus Nilsson¹, Carl-Fredrik Westin², Jan Brabec¹, Samo Lasic³, and Filip Szczepankiewicz^1
1Clinical Sciences Lund, Lund University, Lund, Sweden, ²Brigham and Women's hospital, Harvard Medical School, Boston, MA, United States, ³Random Walk Imaging AB, Lund, Sweden

Probing time-dependence with diffusion MRI enables mapping of microstructure features such as cell sizes (restrictions) and membrane permeability (exchange). However, restrictions and exchange have opposite effects on the MR signal, and cannot be distinguished by just varying the diffusion time. We propose a unified framework for analysis of time-dependent diffusion that enables the design of efficient restriction-exchange correlation experiments. A signal representation was developed featuring parameters connected to restricted diffusion and exchange. This connection was validated by numerical simulations.

Ek T Tan^1,2, Robert Y Shih³, Jhimli Mitra¹, Yihe Hua¹, Tim Sprenger⁴, Chitresh Bhushan¹, Jennifer A McNab⁵, Matt A Bernstein⁶, and Thomas KF Foo^1
1GE Research, Niskayuna, NY, United States, ²Radiology and Imaging, Hospital for Special Surgery, New York, NY, United States, ³Walter Reed National Military Medical Center, Bethesda, MD, United States, ⁴GE Healthcare, Stockholm, Sweden, ⁵Stanford University, Stanford, CA, United States, ⁶Radiology, Mayo Clinic, Rochester, MN, United States

High gradient amplitude, high gradient slew rate, and high peripheral nerve stimulation thresholds are required for oscillating gradient spin-echo (OGSE) diffusion imaging on human MRI systems. With 200 mT/m amplitude and 500 T/m/s slew rate, the MAGNUS head gradient coil was used to evaluate OGSE imaging in six healthy subjects at frequencies up to 100 Hz and b=450 s/mm2. Comparisons were made against standard pulsed gradient spin-echo (PGSE) diffusion in-vivo, which show up to 27% increased OGSE diffusivity, excellent linear correlation with frequency, and correlation length scales of 0.8µm in white matter. Diffusivity changes were negligible in an isotropic phantom.

Aidin Arbabi¹, Jason Kai¹, Ali R Khan¹, and Corey A Baron^1
1Robarts Research Institute, University of Western Ontario, London, ON, Canada

Oscillating gradient spin-echo (OGSE) diffusion MRI enables probing of the frequency content of the apparent diffusion coefficient (ADC). A square root dependence of ADC on frequency has been demonstrated in both healthy and globally ischemic rodent brain tissue, which is consistent with short range structural disorder along neurites. In this work, OGSE data was acquired at multiple frequencies to explore the power law scaling of the ADC in the human brain in vivo, where evidence of a square root dependence of ADC on frequency was obtained for the first time in the in vivo human brain.

Leevi Kerkelä¹, Fabio Nery¹, Feng-Lei Zhou², Geoff J.M. Parker^2,3,4, Filip Szczepankiewicz^5,6,7, Matt G. Hall^1,8, and Chris A. Clark^1
1UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom, ²Centre for Medical Image Computing, University College London, London, United Kingdom, ³Bioxydyn Limited, Manchester, United Kingdom, ⁴CRUK and EPSRC Cancer Imaging Centre in Cambridge and Manchester, Manchester, United Kingdom, ⁵Department of Radiology, Brigham and Women’s Hospital, Boston, MA, United States, ⁶Harvard Medical School, Boston, MA, United States, ⁷Medical Radiation Physics, Clinical Sciences Lund, Lund University, Lund, Sweden, ⁸National Physical Laboratory, Teddington, United Kingdom

Estimation of microscopic fractional anisotropy (μFA) using multidimensional diffusion MRI is a promising novel method for characterising clinically relevant microstructural properties of neural tissue. In this study, three commonly used methods for calculating μFA were compared by imaging a fibre phantom and healthy volunteers. Statistically significant differences were observed in accuracy and precision of the μFA estimates calculated using the covariance tensor model, the gamma distributed diffusivities model, and the direct regression approach. The differences between the methods have to be carefully considered when this promising new metric is applied in characterising microstructural properties of tissue or pathologies.