Mohammad Khateri¹, Marco Reisert ², Alejandra Sierra¹, Jussi Tohka¹, and Valerij G. Kiselev^2
1A.I.Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland, ²Department of Radiology, University Medical Center Freiburg, Freiburg, Germany

Filter-EXchange Imaging (FEXI) evaluates the permeability of cell membranes, which is already used in clinical research. It relies on suppressing the extracellular signal using strong diffusion weighting (mobility filter causing a reduction in the overall diffusivity) and monitoring the subsequent diffusivity recovery. Using simulations, we demonstrate the presence of a spurious exchange: In compartments with complex geometry, there are locations where spins remain unaffected by the mobility filter. Moving to other locations afterward contributes to the diffusivity recovery, thus mimicking exchange. The discovered phenomenon opens large room for investigation towards characterizing the compartment geometry and crystallizing the genuine membrane permeation.

Arthur Chakwizira¹, Filip Szczepankiewicz², Carl-Fredrik Westin³, Geraline Vis⁴, Linda Knutsson^1,5,6, Pia Sundgren^7,8,9,10, and Markus Nilsson^2
1Medical Radiation Physics, Lund, Lund University, Lund, Sweden, ²Clinical Sciences Lund, Lund University, Lund, Sweden, ³Department of Radiology, Brigham and Women's hospital, Harvard Medical School, Boston, MA, United States, ⁴Department of Diagnostic Radiology, Clinical Sciences Lund, Lund University, Lund, Sweden, ⁵Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁶F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ⁷Department of Diagnostic Radiology, Lund University, Lund, Sweden, ⁸Lund University Bioimaging Center, Lund University, Lund, Sweden, ⁹Department for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden, ¹⁰Department of Radiology, University of Michigan, Ann Arbor, MI, United States

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.

Teddy Xuke Cai^1,2, Nathan Hu Williamson^2,3, Rea Ravin^2,4, and Peter Joel Basser^2
1Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, FMRIB, University of Oxford, Oxford, UK, United Kingdom, ²Section on Quantitative Imaging and Tissue Sciences, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, United States, ³National Institute of General Medical Sciences, Bethesda, MD, United States, ⁴Celoptics, Inc., Rockville, MD, United States

The diffusion MR signal in complex tissue such as gray matter exhibits non-Gaussian signal attenuation due to exchange and restrictions. Existing signal models typically ignore one or both effects by assuming Gaussian diffusion or negligible exchange. We propose a more rigorous signal model that incorporates both effects. Subsequently, an acquisition scheme utilizing equal double diffusion encodings ($$$b_1=b_2$$$) at various mixing times, and single diffusion encodings with the same total weighting $$$b=b_1+b_2$$$, is designed to independently characterize the effects of restriction and exchange. The method is tested on live and fixed gray matter specimen using a low-field, high-gradient MR system. 

Ileana O Jelescu^1,2 and Quentin Uhl^1
1Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland, ²CIBM Center for Biomedical Imaging, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland

Fixation alters tissue properties significantly and in vivo vs ex vivo models should be adapted accordingly. Unlike in vivo studies of rodent gray matter (GM) where diffusion time-dependence D(t) was absent for t > 10 ms, allowing an interpretation of time-dependent kurtosis K(t) as resulting from inter-compartment exchange, here we show that ex vivo rodent GM displays marked D(t), with non-Gaussianity arising most probably from extracellular water. K(t) could thus result from combined effect of disorder and exchange. High-b data where extracellular water is preferentially suppressed may still enable the unconfounded estimation of exchange.

Hong Hsi Lee^1,2, Jonas L Olesen^3,4, Qiyuan Tian^1,2, Gabriel Ramos Llorden^1,2, Sune N Jespersen^3,4, and Susie Y Huang^1,2
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, ⁴Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark

The characteristic signal time-dependence in the brain gray matter indicates a significant exchange effect, modeled by an extension of the Kärger exchange model. Here we perform in vivo diffusion measurements with varying time scales (21-40 ms) up to high b-values (≤22 ms/µm²) in a healthy subject on an HCP scanner, and observe the signal time-dependence in frontal lobe, temporal lobe, and limbic system. The estimated residence time is 2-7 ms in the brain gray matter, suggesting that diffusion in neurite and extra-cellular space is coarse-grained at clinical diffusion time scale.

Eloïse MOUGEL¹, Julien Valette¹, and Marco Palombo^2,3,4
1Université Paris-Saclay, Commissariat à l’Energie Atomique et aux Energies Alternatives (CEA), Centre National de la Recherche Scientifique(CNRS), Molecular Imaging Research Center (MIRCen), Laboratoire des Maladies Neurodégénératives, Fontenay aux Roses, France, ²Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, United Kingdom, ³Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, ⁴School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

This work reports first-time measurements of the time-dependent diffusivity and kurtosis of both water and metabolites in vivo in the mouse gray matter (GM). Our aim is to exploit the complementary information provided by the diffusion of water and intracellular metabolites to investigate and potentially disentangle the role of exchange, structural disorder and restriction in GM. Our results show evidence that water diffusion-time dependence in GM is mostly driven by 1D short-range disorder, potentially alongside exchange. Conversely, metabolites’ diffusion-time dependence is exclusively driven by cellular restrictions, paving a new way to quantify noninvasively microstructural restrictions in GM.

Andrada Ianus¹, Joana Carvalho¹, Francisca F. Fernandes¹, Renata Cruz¹, Cristina Chavarrias¹, Marco Palombo^2,3,4, and Noam Shemesh^1
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, ²Centre for Medical Image Computing (CMIC), Dept of Computer Science, University College London, London, United Kingdom, ³Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, ⁴School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

Cross modal validation of micro-architectural features derived from diffusion MRI with contrast from other imaging modalities is critical for developing reliable imaging biomarkers. Here, we map apparent soma and neurite densities via the SANDI methodology in the in-vivo mouse brain at 9.4T and compared the derived metrics with the contrast from the Allen Mouse Brain Atlas which reflects cell body density in the brain. Our findings suggest that soma signal fraction correlates well with atlas intensities in different parts of the brain. 

Tianjia Zhu^1,2, Minhui Ouyang¹, and Hao Huang^1,3
1Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States, ²Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States, ³Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States

Advanced diffusion MRI (dMRI) has enabled noninvasive microstructural assessment that can be only conventionally measured with histology^1-9. However, analytical dMRI models are limited by their restrictive model assumptions, lack of validation, and biased microstructural measures. We have developed Diffusion-MRI based Estimation of Cortical Architecture using Machine-learning (DECAM), a data-driven dMRI-based method accurately estimating cortical soma and neurite densities (SD and ND) in the cortex¹⁰ leveraging a variety of complementary dMRI contrasts. By providing high-fidelity estimated soma and neurite density maps validated with histology, DECAM paves the way for data-driven noninvasive virtual histology for potential applications such as Alzheimer’s diseases.