Hong-Hsi Lee¹, Els Fieremans¹, Susie Y Huang², Qiyuan Tian², and Dmitry S Novikov^1
1New York University School of Medicine, New York, NY, United States, ²Department of Radiology, A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States

In vivo estimation of cell dimension using diffusion MRI usually requires biophysical modeling of multiple compartments in biological tissues. However, the signal decomposition of multiple compartments is non-trivial due to limited amount of data and scan time. Instead, by applying strong diffusion gradients, a universal localization regime emerges: Magnetization far away from the cell boundaries vanishes, and only that near the boundaries within a thickness of localization length contributes to signal. Here, using Connectome gradients, we for the first time achieve the localization regime in vivo, and estimate the soma size in cortical brain gray matter of two healthy subjects.

Alexandre de Skowronski¹, Marco Palombo², Dmitry S. Novikov³, and Ileana O. Jelescu^4
1Dept. of Physics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ²Centre for Medical Image Computing and Dept. of Computer Science, University College London, London, United Kingdom, ³Center for Biomedical Imaging, Dept. of Radiology, New York University, New York, NY, United States, ⁴CIBM Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

Developing a relevant model for brain gray matter is a complex task. As opposed to white matter, features such as inter-compartment water exchange or soma should likely be modeled. In this work we examine the performance of a variant of the Kärger Model, called GRAMMI, that accounts for exchange, both on synthetic and experimental data. We show q-t coverage is necessary for reliable model parameter estimation at the individual voxel level and compare two regression approaches. Future work includes protocol optimization and the extension of the GRAMMI model to account for soma.

Maëliss Jallais¹, Pedro L. C. Rodrigues¹, Alexandre Gramfort¹, and Demian Wassermann^1
1Université Paris-Saclay, Inria, CEA, Palaiseau, France

We propose a new method to solve the inverse problem of relating the diffusion MRI signal with cytoarchitectural characteristics in brain gray matter. Specifically, our method has quantitative sensitivity to soma density and volume. Our solution is twofold. First, we propose a new forward model that relates summary statistics of the dMRI signal with tissue parameters, relying on six b-shells only. We then apply a likelihood-free inference based algorithm to invert the proposed model, which not only estimates the tissue parameters that best describe the acquired diffusion signal, but also a full posterior distribution over the parameter space.

Marco Palombo¹, Daniel C. Alexander¹, and Hui Zhang^1
1Centre for Medical Image Computing, University College London, London, United Kingdom

Diffusion-weighted MRI (dMRI) is a formidable technique for non-invasively characterizing brain microstructure. Biophysical modelling is often necessary to gain specificity to cellular structure. However, designing sensible biophysical models and appropriate dMRI acquisitions is challenging, especially for gray matter (GM), as little is known about typical values of relevant features of brain-cell morphology contributing to dMRI signal. This study addressed this unmet need: we analysed ~3,500 cells from mouse, rat, monkey and human brains to determine statistical distributions of 13 morphological features relevant to GM microstructure modelling. Illustrative examples demonstrate how this study can inform biophysical modelling. 

Jonas L. Olesen^1,2, Noam Shemesh³, and Sune N. Jespersen^1,2
1Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Aarhus University, Aarhus, Denmark, ²Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark, ³Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal

Diffusion models can be validated by observing their functional dependencies, exemplified by the b^-1/2 power-law scaling recently used to validate the “stick” compartment in white matter. In contrast, such behavior has not been observed in grey matter (GM), potentially due to a) water exchange between dendrites and extra-neurite space and/or b) a distinct signal contribution from somas. We report the first observation of the stick power-law in GM at very large b-values consistent with b). Nevertheless, the dependence on diffusion time indicates significant water exchange affecting the scaling range. The combined observations thus offer a window into more complicated microstructure.

Ileana O. Jelescu^1,2 and Dmitry S. Novikov^3
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, ²Animal Imaging and Technology, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland, ³Dept. of Radiology, New York University School of Medicine, New York, NY, United States

One fundamental challenge in brain microstructure is to establish the biophysical origin of effects beyond the “Standard Model” (SM) picture of non-exchanging Gaussian compartments. The intra-compartmental structural disorder competes with inter-compartmental water exchange. Here we show that in rats, the exchange dominates, and offer the picture of diffusion time effectively filtering out the contribution of unmyelinated axons with stronger dispersion. At long times, only the myelinated (non-exchanging) axons contribute to the intra-axonal SM compartment, and the rest is attributed to extra-axonal space.

Mariam Andersson^1,2, Marco Pizzolato^1,2,3, Hans Martin Kjer^1,2, Henrik Lundell¹, and Tim B. Dyrby^1,2
1Danish Research Centre for Magnetic Resonance, Hvidovre, Denmark, ²Technical University of Denmark, Kgs. Lyngby, Denmark, ³Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

Orientation dispersion bias in axon diameter measurements can be removed by powder averaging of the diffusion MRI signal in isotropically distributed directions, but has not been validated in complex fiber architectures. Here, we demonstrate the success of the spherical mean technique (SMT) and a recent power law (PL) implementation in removing orientation-related bias in diameter estimates of real axons from the splenium corpus callosum and a complex crossing fiber region of the vervet monkey brain. In the crossing fiber region, we find a significant population of very large axons, indicating a need for sensitivity to a wide range of diameters.

Maryam Afzali¹, Markus Nilsson², Marco Palombo³, and Derek K Jones^1
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, ²Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden, ³Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom

Soma and Neurite Density Imaging (SANDI) was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges were identified; First, the Rician noise floor biases estimation of soma properties. Second there is an empirical lower bound on the spherical signal fraction and pore-size. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, estimation of spherical pore-size is challenging.

Markus Nilsson¹, Samuel St-Jean^1,2, Christian Beaulieu², and Filip Szczepankiewicz^1
1Clinical Sciences Lund, Lund University, Lund, Sweden, ²Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada

The intra-axonal axial diffusivity (D_a) has a prominent role in describing and modeling the white matter microstructure, but cannot be obtained from regular diffusion tensor imaging due to the influence of orientation dispersion and extracellular water. It may be estimated using higher b-values and modeling, however, its estimation is still an ill-posed problem or requires knowledge of the orientation distribution function. Here, we show that using b-tensor encoding and powder averaging turns the problem into a well-posed one and allows rapid mapping across the whole brain yielding D_a values of 2.2-2.7 µm²/ms.

Jacob-Jan Sloots¹, Martijn Froeling¹, Geert Jan Biessels², and Jaco Zwanenburg^1
1Radiology, University Medical Center Utrecht, Utrecht, Netherlands, ²Neurology, University Medical Center Utrecht, Utrecht, Netherlands

The apparent diffusion coefficient (ADC) in brain tissue slightly varies over the cardiac cycle. In this study, we investigate to what extent ADC variations can be explained by brain tissue strain, which affects the measured MRI signals. To this end, we developed a high-field MRI sequence that simultaneously measures both ADC and tissue strain. Preliminary results in 2 volunteers show that ADC fluctuations over the cardiac cycle are an order of magnitude larger than could be explained from measurement errors induced by tissue strains. Consequently, ADC fluctuations in the brain probably reflect physiology.