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In this study, 42 patients with brain tumor were analyzed using mb-DTI and s-DTI in the arcuate fasciculus and corticospinal tract, two critical white matter tracts for language and motor function, respectively. The phantom was constructed to mimic a capillary bed and allowed for the controlled application of fluid flow at varying rates.

Advanced IVIM MRI methods beyond the biexponential pseudo-diffusion model were shown to be capable of accurately characterizing fluid flow inside a capillary network yielding intuitive parameters in a reproducible manner. Accurate predictions of the drug concentration via computational fluid dynamics models are essential, and models not accounting for diffusion non-Gaussianity give predictions that are not in good enough agreement with experimental results. We make recommendations on the use of fluid dynamics models in the clinical setting.

It relies on a CUDA-based collision solver that enables to reach high values of packing density and angular dispersion, even in the case of multiple fiber populations. MEDUSA thus achieves the fast construction of biomimicking white matter phantoms with fully controlled geometrical properties, including axons, astrocytes and oligodendrocytes. We computed that the resolution limit for axon diameters of our dMRI data is about 1. It is conceivable, however, that it partly originates from relaxation weightings of different compartments. In this study, we investigate the diffusion time dependence of the diffusion tensor in brain tissue at 1.

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The results show clear time dependencies of axial and radial diffusivity. The simulated 3D geometries feature randomly-placed cylinders with diameter heterogeneity. The approach is validated on a public dataset of ex vivo cat spinal cord and exhibits indices of axon density and of the mean and standard deviation of the axon diameter distribution in agreement with histological measurements. The method is shown to outperform AMICO, which relies on approximate analytical expressions for the signal, and a simpler model using Monte Carlo simulations in a homogeneous packing of identical cylinders.

The method was evaluated on simulated diffusion MRI signals obtained through Monte Carlo simulations, using intermediate diffusion times, mimicking both ex-vivo and in-vivo conditions. HOTmix provided better reconstructions compared to the standard diffusion tensor, the kurtosis tensor, and a single generalized higher order tensor. In future work, we will explore whether modelling the hindered compartment using HOTmix improves microstructural features estimated using dMRI.

The information contained in the kurtosis tensor, accessed by SDE, is insufficient to recover biophysical model parameters from the MR signal.

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We prove that Double Diffusion Encoding DDE , letting us additionally access the full diffusion tensor covariance, makes parameter estimation well-posed. These delays have a range of a few milliseconds and can thus be observed in bioelectric recordings such as electroencephalography and magnetoencephalography MEG. This approach identifies information flow between cortical regions using a model where transmission delays are parameters. The delays which maximize information transfer are identified and, using streamline length obtained through tractography, are then converted to axon diameters.

We present results obtained on four subjects of the Human Connectome Project. In using both in vivo and formalin fixed ex vivo methods, we sought to better understand what parameters in dMRI may be used to understood be as PVS in brain tissue. RD decreased and FA increased with increasing diffusion time. Moreover, the changes of white matter fODF in the white matter crossing fiber area at different diffusion times are shown. Two random walk simulation methods are presented which show effects of cortical laminar microstructure on ADC and K.

This could open possibilities of detecting cortical cytoarchitecture, i. Our approach applies multiplicative filters to the diffusion tensor distribution DTD functions of the underlying tissue to obtain more specific information of tissue microstructure using the statistical moments of the filtered DTD's. We propose several descriptive statistics to distinguish microstructures that cannot be separated using existing methods.

These measures are demonstrated in simulations and in vivo data acquired from a human brain. In this technique microscopic tissue features are estimated by using different gradient waveforms that typically have different time regimes. This work studies for the first time two potential time-dependence issues that arise from these methods in-vivo in the human brain.

We detect time-dependence effects in both cases, and even though these are small on a clinical system, they should not be overlooked. Here, we cleared a 1. This work demonstrates one of the largest and highest quality CLARITY cuboids from a macaque brain and explores key steps in the co-registration and analysis required to make a robust comparison against DTI data.

The model exhibits the ability to estimate microstructural parameters from both numerically simulated and experimentally acquired dMRI data suggesting that random forests, and machine learning more generally, may be a useful tool in dMRI microstructure estimation of skeletal muscle. Umberto Villani, Erica Silvestri, Marco Castellaro, Maurizio Corbetta, Alessandra Bertoldo Most existing biophysical models used to quantify diffusion microstructural information are designed for the healthy brain.

When pathological processes occur, the diffusion signal of cancerous regions is altered, possibly biasing the fitting results of these models and making their parameter estimates unreliable. In this work, we investigate the precision and accuracy of estimates provided by several well known diffusion models, by evaluating their goodness-of-fit with residual sum of squares map and the parameter reliability with the bootstrap technique.

Kurtosis tensors and microstructural parameters were used for statistical analysis using a LME model. The results show a strong relation between disability and kurtosis tensor parameters similar to observations in other hypomyelinating MS models and in patients. Conversely, changes in model parameters, such as extra-axonal axial diffusivity, are clearly different from previous studies using other animal models of MS.

The non-uniform T2 relaxation has been shown to affect measurements of apparent diffusion coefficients ADC.

In additional to ADC, diffusion heterogeneity measured by modeling non-monoexponential decay has been used to characterize water diffusion in tissue microstructure. The purpose of this study is to study the effects of non-uniform T2 relaxations on the diffusion heterogeneity using a Monte Carlo simulation. Significant age-related increases in both MK and FA were found in all tracts. Distinctive developmental trajectories of WM tracts with MK measurements compared to those with FA measurements were revealed, indicating heterogeneous increases of microstructural complexity among WM tracts.

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A DDE protocol with a reduced number of acquisitions has recently been proposed as a solution. Classification and regression are performed using Extremely Randomized Trees from OGSE signals corresponding to 6 distinct frequencies and synthesized from numerical simulations in realistic white matter phantoms depicting beaded axons. Using the cumulant expansion up to 2nd order in the b-tensor, it has been recently shown that multidimensional diffusion encoding makes the model parameter estimation problem well-posed. However, the tensorial properties of the expansion and their relationship with the SM assumptions have not been exploited.

We reformulate the solution of the SM in an elegant tensorial form and analyse the constraints the SM imposes, showing how they can potentially falsify the model. A diffusion model is required to derive inferences about the microstructure from the time dependent eigenvalue data. The tertiary eigenvalue at the largest diffusion time was significantly different between young and senior cohorts.

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Model derived myofiber size decreased and free diffusion coefficient increased with age, though neither parameter reached significance. However, its impact on estimating brain microstructural features has only been studied in a handful of parametric models. Building statistics through stratified bootstrap, we show that spherical encoding substantially increases the variance in estimated parameters and should be avoided.

Planar encoding, on the other hand, did not offer clear improvement or worsening within our current acquisition scheme and setup. Recent approaches to estimating apparent axon diameter in white matter have employed spherical averaging to avoid the confounding effects of fiber crossings and dispersion at the expense of losing sensitivity to effective compartment size. Here, we investigate the feasibility and benefits of incorporating higher-order spherical harmonic SH components into a rotationally invariant axon diameter estimation framework and demonstrate improved precision of axon diameter estimation in the in vivo human brain.

The connection between AXR and permeability has already been validated for simplified two-compartment models. However, multi-compartment systems and non-periodic geometries have not been evaluated so far. In this study, we were able to show that it is also possible to reliably connect permeabilities to AXR-values for these geometries. Furthermore, the AXR was only dependent on average cell size and not on the number of compartments.

In this work we compared different multi-fascicle computational frameworks by assessing their impact on microstructure properties. Specifically, we investigated the impact after geometrical transformation and averaging of multi-fascicle models, two key operations when carrying out population studies. More surprisingly, the values of microstructural descriptors depended on the number of subjects.

The quaternion framework, in contrast, was the best at preserving microstructural features. Distribution mapping is challenging: current methods simplify it by either estimating the mean diameter, imposing parametric distributions, or combining non-parametric approaches with Double Diffusion Encoding.

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We present a non-parametric framework based on a PGSE protocol. Simulations show robust reconstruction of unimodal and bimodal distributions. The method is sensitive to population specific changes within bimodal distributions, as long as the underlying populations are separated by a minimum distance. Possible uses of such a model are outlined, such as its use in allowing muscle tractography results to regularized by neighboring voxels in its defined muscle tract. In this abstract, we will introduce a method called asymmetric spectrum imaging ASI to improve estimation of white matter pathways in the baby brain by 1 incorporating an asymmetric fiber orientation model to help resolve subvoxel fiber configurations such as fanning and bending, and 2 explicitly modeling the spectrum of diffusion typical in the developing brain.

However, its diffusion MRI profile has not been studied. Here we show experimental evidences that that PVS can be measured with diffusion MRI and the signature of this compartment is anisotropic. The theory underlying FBI predicts the b-value dependence for the dMRI harmonic power of any given degree as long as the b-value is sufficiently large. Good agreement between theory and experiment has been previously demonstrated for the zero-degree harmonic power.

Here the predicted functional forms for higher degree harmonics are shown to also agree well with experimental measurements, providing additional support for the validity of FBI. Annelinde Buikema, Arjan Dekker, Jan Sijbers The purpose of this work is to study the effect of varying the diffusion time on the estimation of the parameters of the two-compartment diffusion tensor model in the mid-time regime. Simulation results show that the precision of the diffusion time-dependent compartmental parameter estimates increases when a variable echo time acquisition scheme is used.

At low SNR, however, including diffusion time-dependence may lead to a high bias and variance compared to the more conventional non diffusion time-dependent model. We found that this is mainly due to the greater water content in the developing brain. However, robust computational tools are needed for population imaging studies, so we developed a computational framework MicroQIT using the Quantitative Imaging Toolkit to meet this need by providing regional summaries and spatially normalized microstructure parameter maps. It supports a variety of ways to extract microstructure information from multi-shell diffusion MRI, leverages grid computing environments, and is available for use by the research community for future studies.