Jonathan Scharff Nielsen¹ and Manisha Aggarwal^1
1Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States

To investigate the influence of exchange on microscopic anisotropy estimates derived from kurtosis differences between linear-tensor-encoding (LTE) and spherical-tensor-encoding (STE) diffusion weighting waveforms, random walk simulations were performed in a substrate of packed spheres of varying permeability. A pronounced μFA bias was observed with spectrally detuned LTE due to restricted diffusion and kurtosis time dependence, decreasing with rate of exchange, but also with tuned LTE due to inherent differences in exchange sensitivity, increasing with exchange. Our results suggest a need for caution in interpreting MDE-derived microscopic anisotropy estimates in the presence of exchange.

Arthur Chakwizira¹, Samo Lasič², Alexis Reymbaut³, Carl-Fredrik Westin⁴, Filip Szczepankiewicz⁵, and Markus Nilsson^5
1Medical Radiation Physics, Lund, Lund University, Lund, Sweden, ²Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Amager and Hvidovre, Copenhagen, Denmark, ³Random Walk Imaging, AB, Lund, Sweden, ⁴Department of Radiology, Brigham and Women's hospital, Harvard Medical School, Boston, MA, United States, ⁵Clinical Sciences Lund, Lund University, Lund, Sweden

Temporal velocity correlations of diffusing particles carry information about the structure of the diffusion environment. Studying high-order velocity autocorrelation functions can aid in understanding how signal is influenced by restricted diffusion and exchange, and in turn how to design experiments that are sensitive/independent to these phenomena. In this work, we employ numerical simulations on a variety of substrates to demonstrate this notion. We find that the fourth order velocity autocorrelation bears a distinctive signature of exchanging systems. In addition, we highlight that the effect of exchange on the second order velocity autocorrelation is negligible when exchange is barrier-limited.

Kulam Najmudeen Magdoom^1,2,3, Michal E. Komlosh^1,2,3, Saleem Kadharbatcha^1,3, Dario Gasbarra⁴, and Peter J. Basser^2,3
1The Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF) Inc.,, Bethesda, MD, United States, ²Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Healt, Bethesda, MD, United States, ³Center for Neuroscience and Regenerative Medicine, Uniformed Services University of Health Sciences, Bethesda, MD, United States, ⁴University of Helsinki, Helsinki, Finland

Neural tissue microstructure plays a key role in developmental, physiological, and pathophysiological processes. Diffusion tensor distribution (DTD) MRI is a promising approach to resolve sub-voxel microstructural features using multiple diffusion encodings. In this study, we have applied a novel DTD framework and pulse sequence to investigate its capabilities in revealing neural tissue microstructure. We present high resolution data acquired using an excised visual cortex and cervical spinal cord of a macaque monkey. The results show DTD MRI untangles size, shape and orientation heterogeneity within a voxel and parcellates tissue consistent with histological findings.

Ricardo Coronado-Leija¹, Hong-Hsi Lee², Ali Abdollahzadeh³, Jussi Tohka³, Alejandra Sierra³, Els Fieremans¹, and Dmitry S Novikov^1
1Radiology, New York University School of Medicine, New York, NY, United States, ²Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, ³University of Eastern Finland, Kuopio, Finland

In this work, we perform Monte Carlo simulations of diffusion in the extra-axonal space segmented from realistic 3D electron microscopy substrates. Simulations in sham and TBI rat brains confirm the universality of the power-law functional form of the axial and radial time-dependent diffusion $$$D^{\parallel,\perp}(t)$$$ and kurtosis $$$K^{\parallel,\perp}(t)$$$. We characterize the changes caused by TBI, finding that the dependence of long-time asysmptote $$$D^{\perp}_\infty$$$ on the extra-axonal volume fraction follows Archie's law. We also validate the theoretically predicted relationship between the power-law tails of $$$D(t)$$$ and $$$K(t)$$$. 

William Richard Warner¹, Andrada Ianus², Noam Shemesh², Malwina Molendowska³, Derek Jones³, Flavio Dell'Acqua⁴, Marco Palombo^3,5, and Ivana Drobnjak^1
1Computer Science Department, University College London, London, United Kingdom, ²Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, ³Cardiff University Brain Research Imaging Centre, Cardiff, United Kingdom, ⁴NatBrainLab, King's College London, London, United Kingdom, ⁵School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

Temporal Diffusion Ratio (TDR) is a recently proposed dMRI technique, with potential for mapping areas of restricted diffusion. We optimise the TDR diffusion sequences in simulation to provide maximum contrast for a range of restricted structures, finding sequence shapes and the optimal HARDI subset size for calculating TDR. We then demonstrate the results experimentally in-vivo on the human brain, and ex-vivo using a pre-clinical scanner on the mouse brain.
 

Nathalie Just¹, Samo Lasič^2,3, Ditte Bentsen Christensen⁴, Julien Valette⁵, Tim Dyrby^2,6, Hartwig Siebner², and Henrik Lundell^2
1Danish Research Center for Magnetic Resonance, Copenhagen University Hospital - Amager and Hvidovre, Hvidovre, Denmark, ²Danish Research Center for Magnetic Resonance, Copenhagen University Hospital Amager and Hvidovre, Hvidovre, Denmark, ³Random Walk Imaging, Lund, Sweden, ⁴HYPERMAG, DTU, Copenhagen, Denmark, ⁵MIRCen, Commissariat à l' energie atomique et aux Energies Alternatives, Fontenay-aux-Roses, France, ⁶Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark

Metabolite diffusion provide the unique ability to study the intracellular environment of specific cell types of the brain. Multiple studies suggest that the diffusivity along dendrites and axons is time dependent due to variations in diameter. We explore the signal due to such effects in Monte Carlo simulations in settings feasible for preclinical PGSE measurements and find that time dependent kurtosis, but not diffusivity provide the most potent source of contrast to this effect. We observe similar effects of intraneuronal NAA diffusion in rat.

Ying Liao¹, Santiago Coelho¹, Wafaa Sweidan¹, Jenny Chen¹, Dmitry S. Novikov¹, and Els Fieremans^1
1Radiology, NYU School of Medicine, New York, NY, United States

Conventional diffusion MRI (dMRI) techniques, such as DTI and DKI, are sensitive to pathology but lack specificity. In brain white matter, the “Standard Model” framework of dMRI may provide  specificity to microstructural changes. Generally, clinical dMRI is noisy and limited, making SM estimation challenging. Thus, different constraints and techniques have been introduced to robustly extract SM parametric maps. Here, we employ a large clinical dataset of Multiple Sclerosis patient data (N = 134) and noise propagation experiments to study the sensitivity and specificity of these techniques.

Jan Malte Oeschger¹, Karsten Tabelow², and Siawoosh Mohammadi^1,3
1Institute of Systems Neurosciences, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ²Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany, ³Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

The recently introduced axial-symmetric DKI framework is less noise sensitive but unable to capture more complex fiber configurations causing an AxDKI inherent bias in those voxels. We found that the parallel and perpendicular kurtosis are most often affected by this inherent bias and that it translates to the AxDKI based biophysical parameters. Bias-free estimation of the biophysical parameters with the AxDKI framework, therefore, is difficult if not impossible at this point. However, the parallel and perpendicular diffusivity and the mean kurtosis were largely bias-free, encouraging use of the AxDKI framework in studies where these parameters are focused on.

Timo Roine^1,2 and Sila Genc^3
1Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland, ²Turku Brain and Mind Center, University of Turku, Turku, Finland, ³Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom

We investigated the test-retest reliability of  bundle- and voxel-/fixel-level microstructural metrics with fixel-based analysis, neurite orientation dispersion and density imaging, and diffusion tensor imaging. For the bundle-level analyses, TractSeg was used to automatically segment 72 fiber bundles of the brain. Our results indicate that most of the metrics, especially those measured with fixel-based analysis, are highly robust and show excellent test-retest reliability both in local and bundle-level analyses. In general, the reproducibility was higher in the white matter in contrast to gray matter and for acquisitions with multi-shell compared to single-shell data.

Naila Rahman^1,2, Kathy Xu², Arthur Brown^2,3, and Corey Baron^1,2
1Medical Biophysics, Western University, London, ON, Canada, ²Robarts Research Institute, London, ON, Canada, ³Anatomy and Cell Biology, Western University, London, ON, Canada

Imaging markers of mild to moderate concussion are notoriously difficult to detect in vivo. Advanced diffusion MRI (dMRI) techniques have shown increased sensitivity and specificity to microstructural changes in various disease and injury models. Oscillating gradient spin-echo (OGSE) dMRI is sensitive to structural disorder and microscopic anisotropy (µA) dMRI is sensitive to water diffusion anisotropy independent of neuron fiber orientation. In this work, we demonstrate that both microscopic fractional anisotropy and diffusion dispersion show acute sensitivity to concussion, while traditional diffusion MRI markers do not.

Laurin Mordhorst¹, Mohammad Ashtarayeh¹, Maria Morozova^2,3, Sebastian Papazoglou¹, Björn Fricke¹, Tobias Streubel^1,2, Carsten Jäger², Henriette Rusch³, Ludger Starke⁴, Thomas Gladytz⁴, Ehsan Tasbihi⁴, Thoralf Niendorf^4,5, Nikolaus Weiskopf^2,6, Markus Morawski^2,3, and Siawoosh Mohammadi^1,2
1Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, ²Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, ³Paul Flechsig Institute of Brain Research, University of Leipzig, Leipzig, Germany, ⁴Berlin Ultrahigh Field Facility, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, ⁵Experimental and Clinical Research Center, a joint cooperation between the Charité Medical Faculty and the Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany, ⁶Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig, Germany

The axon radius is a main determinant of the conduction velocity of action potentials. Robust, MRI-based axon radius estimation is sensitive to a tail-weighted estimate of the ensemble-average axon radius, i.e., the effective axon radius ($$$r_{\text{eff}}$$$). It is unclear how $$$r_{\text{eff}}$$$ translates into the arithmetic mean axon radius ($$$r_{\text{arith}}$$$), which may be more indicative of the conduction velocity than $$$r_{\text{eff}}$$$. We investigate the feasibility to predict $$$r_{\text{arith}}$$$ from $$$r_{\text{eff}}$$$ using linear regression on high-resolution, large-scale light-microscopy images of a human corpus callosum sample and validate this linear relationship with microscopy and diffusion-weighted MRI images of another human corpus callosum sample.

Christoph Martin Stuprich¹, Tristan Anselm Kuder², Bernhard Hensel³, Michael Uder¹, and Frederik Bernd Laun^1
1Radiology, University Hospital Erlangen, Friedrich Alexander University Erlangen, Erlangen, Germany, ²German Cancer Research Center(DKFZ) Heidelberg, Heidelberg, Germany, ³Friedrich Alexander University Erlangen, Erlangen, Germany

In Diffusion MR, short gradient pulses are often ascribed to generate oscillating signal curves. These characteristic oscillations or “peaks” appear if a certain characteristic length-scale in the underlying tissue is present. Mitra and Halperin mention the possibility of “Bragg Peaks” when using long gradient pulses. In this work we explicitly show and interpret the occurrence of such signal peaks under application of long gradient pulses in theory and simulation.

Lukas Lundholm¹, Mikael Montelius¹, Oscar Jalnefjord^1,2, Eva Forssell-Aronsson^1,2, and Maria Ljungberg^1,2
1Medical Radiation Sciences, University of Gothenburg, Gothenburg, Sweden, ²Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden

VERDICT MRI provides estimates of intracellular volume fraction and cell radius non-invasively which may facilitate e.g., tumor grade classification and longitudinal studies without the need for biopsy. Tumors of a human SI-NET animal model were irradiated and measured with diffusion MRI. Colormaps of cell radius index and intracellular fraction were derived from both VERDICT analysis of the MR data and histological analysis of stained tumor slices. VERDICT maps of intracellular fraction corresponded well with histology in necrotic tissue, however the cell radius index was poorly estimated in these regions. Further work is needed to optimize VERDICT for different tissue types.