Keywords: Microstructure, Diffusion/other diffusion imaging techniques, Diffusion modeling, Tissue characterization, Soma, Exchange

Motivation: Gray Matter lacks a unified microstructure model, unlike White Matter. This study introduces the Generalized Exchange Model (GEM) to unify Gray Matter models, introducing exchange with simple structures.

Goal(s): Evaluate GEM performance, compare it with other Gray Matter models, and assess its potential for clinical MRI.

Approach: GEM parameters and equations are detailed and validated with simulated data. GEM is applied on clinical MRI data, and compared to other Gray Matter model estimates.

Results: GEM successfully unifies Gray Matter diffusion models, offering plausible estimations in clinical MRI revealing microstructural patterns. Future research will optimize data acquisition and assess accuracy.

Impact: The introduction of the Generalized Exchange Model holds significant implications. For scientists, it provides a unified framework, potentially simplifying and enhancing Gray Matter modeling, promoting consistency in research. Clinicians may benefit from improved specificity in diagnosing neurological conditions.

Keywords: Microstructure, Microstructure, diffusion, brain, exchange, gray matter, water, microstructure, simulation, spines, dendrites

Motivation: Diffusion exchange models NEXI and SMEX assumes permeative exchange between intra- and extra-neurite compartments. Here, we hypothesize fine microstructures, like spines in dendritic segments, without permeable membranes can mimic permeative exchange.

Goal(s): We aim to emphasize the significance of taking diffusion-mediated exchange into account when interpreting model-based estimates of exchange in intricate microstructures like the gray matter of the brain.

Approach: Monte-Carlo water diffusion simulations in spiny dendritic branches, also featuring undulations and beading

Results: Our results question the way we interpret dMRI exchange estimates, emphasizing the need to exercise caution when inferring these estimates solely as indicators of membrane permeability.

Impact: Diffusion exchange models assumes permeative exchange between intra- and extra-neurite compartments. We hypothesize fine microstructures without permeable membranes can mimic permeative exchange. We aim to emphasize considering the impact of intricate microstructures in gray matter when interpreting model-based exchange estimates

Keywords: Microstructure, Microstructure

Motivation: New biophysical models of gray matter microstructure have been introduced, with a particular focus on exchange time and soma size estimations. However, the fitting quality of these models has not been studied.

Goal(s): Our goal is to study the feasibility of estimating both exchange time and soma size in a clinical setting.

Approach: We applied µGUIDE, a Bayesian inference framework, to quantify the quality of the fitting of two models from the literature, NEXI and SANDIX, using an extensive protocol, and a clinical one.

Results: Estimations of both exchange time and soma size in clinical setting shows high uncertainty.

Impact: For the first time, we are using µGUiDE on two microstructure models that take into account the exchange between neurites and the extra-cellular space. We applied it to both synthetic and clinical data.

Keywords: Microstructure, Brain

Motivation: Transcytolemal water exchange time (t_ex) can be estimated using diffusion-time-dependent diffusion kurtosis imaging (tDKI) acquired at long diffusion times(t_d). However, dMRI signals acquired at long t_d's using STEAM sequence are noisy, fitting of tDKI model accumulates errors.

Goal(s): proposed a Bayesian strategy to improve the accuracy and robustness of t_ex mapping based on the Kärger model (KM). 

Approach: we fitted the t_ex map based on the simulation and in vivo human brain data using Bayesian and the nonlinear least square methods to compare the fitting results.

Results: Bayesian fitting significantly reduced the estimation error and variance in the simulation and in vivo scan. 

Impact: The proposed a Bayesian strategy significantly reduced the estimation error and variance and improved microstructural maps in vivo. And the proposed 10-minute t_d-dMRI protocol showed potential value for water exchange mapping in the human brain in clinical practice.

Keywords: Microstructure, Microstructure, self-superivsed, physics-informed

Motivation: Diffusion modeling is an important tool for quantifying microstructure properties from diffusion data, but its optimization is computationaly expensive.

Goal(s): To achieve rapid microstructure model parameter estimation while outperforming conventional methods.

Approach: DIMOND employs a neural network (NN) to map input diffusion data to model parameters and optimizes NN by minimizing the difference between the input data and the synthetic data generated via the diffusion model parametrized by NN outputs.

Results: DIMOND outperforms conventional methods for fitting kurtosis and NODDI models in terms of metric accuracy. DIMOND reduces NODDI model fitting time from hours to minutes, or even seconds by leveraging transfer learning.

Impact: DIMOND has a high potential to transform diffusion model fitting. Its self-supervised training paradigm, high efficacy and efficiency may dramatically improve the feasibility and accessibility of diffusion MRI based microstructure and connectivity mapping in clinical and neuroscientific applications.

Keywords: Simulation/Validation, Microstructure, gray matter

Motivation: Virtual microstructure has been one of the promising approaches to study and validate diffusion MRI microstructure models. Previous works in generating synthetic substrates have only focused on specific cell types, i.e. axons, neurons.

Goal(s): The main goal of this work is to advance modeling of virtual gray matter microstructure and to close the real to simulation gap in brain microstructure geometries.

Approach: We extend a framework of microstructure modeling to generate hybrid substrates that combine purely synthetic cells with virtual cells reconstructed from histology.

Results: Different virtual substrates have been generated with axons and neurons with similar volume fractions in human cortex.

Impact: More realistic virtual microstructure can allow the development of new computational models to map microstructure from diffusion MRI data. It can also be used to validate a whole set of analytical models and evaluate their accuracy.

Keywords: Diffusion Modeling, Modelling, disorder, time-dependent diffusion

Motivation: Characterizing water diffusion in its long-time regime is relevant to most medical applications of diffusion MRI. However, this process is challenging to model even for a one-dimensional structure with semipermeable membranes.

Goal(s): Developing methods that predict the asymptotic instantaneous diffusivity from the bulk diffusivity and the membranes’ locations and permeabilities.

Approach: We studied the problem theoretically and expressed the instantaneous diffusivity as an infinite sum. An independent numerical scheme is developed. Several types of disorder in the membranes’ positions were considered.

Results: Our findings show excellent agreement with simulations. Our methods provide an alternative means for studying time-dependent diffusion.

Impact: This report provides an improved understanding of how tissue organization and disorder may affect water diffusivity, thus making it relevant to diffusion MR studies characterizing tissue microstructure. We also provide methods for readily estimating the instantaneous diffusivity.

Keywords: Microstructure, Microstructure

Motivation: $$$R_2$$$ in WM is orientation dependent due to microscopic magnetic anisotropy. So far, the Standard Model of diffusion (SM) has been extended to only include isotropic $$$R_2$$$ (TEdDI).

Goal(s): Our goal is to characterize $$$R_2$$$ anisotropy of a multi-echo dMRI signal for long diffusion times and incorporate $$$R_2$$$ anisotropy into TEdDI (STEdDI).

Approach: We simulate $$$R_2$$$ of PGSE signal in magnetized cylinders using Monte-Carlo, and fit TEdDI and STEdDI on ex vivo mouse multi-echo dMRI data acquired at 16.4T.

Results: $$$R_2$$$ anisotropy outside axons are non-axially-symmetric, depends on B0 direction, gradient direction and b-value. Residuals are significantly lower with STEdDI in dMRI data.

Impact: Interplay between microscopic magnetic fields and diffusion weighting affects $$$R_2$$$ in extra-axonal space. Incorporating $$$R_2$$$ anisotropy$$$\,$$$into modeling lowered the residuals and may allow rotation-free estimation of $$$R_2$$$ anisotropy, which could be useful to gain a deeper insight into brain microstructure.

Keywords: Microstructure, Microstructure, Exchange

Motivation: Time-dependent diffusion MRI reveals fast exchange in grey matter, but it remains unclear whether permeative or non-permeative exchange explains these findings.

Goal(s): We set to investigate the effect of non-permeative exchange induced by dendritic spines on the time-dependence of the diffusion-weighted signal and the exchange estimates obtained from the signal.

Approach: Monte Carlo simulations were performed in synthetic dendrites with varying spine densities and signals were generated using free gradient waveforms. The intracellular signals were analysed using a restriction-exchange framework from previous work. 

Results: Dendritic spines give the same signal time-dependence as permeative exchange. Estimated exchange rates increase with spine density.

Impact: Dendritic spines may explain the exchange rates observed with diffusion MRI in grey matter. Furthermore, exchange measurements provide a potential biomarker of dendritic spine morphology, which is important because spine density is implicated in, for example, learning and psychiatric disorders.

Keywords: Microstructure, Machine Learning/Artificial Intelligence, Q-space, QTI, Microscopic Anisotropy, µFA

Motivation: Tensor-encoded diffusion MRI (dMRI) methods for tissue microstructure elucidation typically require lengthy dMRI acquisitions and computationally costly, SNR-sensitive data analysis.

Goal(s): Employing q-space trajectory imaging (QTI), we seek to greatly reduce both the number of required measurements and computational burden in analysis for robust estimation of parameters quantifying brain tissue microstructure.

Approach: A machine learning-based estimator is trained on a 10-fold reduced subset of an extensive dMRI protocol acquired in 18 healthy volunteers.

Results: The proposed method outperforms a state-of-the-art model fitting framework, yielding smoother parameter maps and showing lower deviation from the chosen ground truth, even at reduced SNR/increased resolution.

Impact: Quantitative measures of brain microstructure are obtained by accelerated tensor-encoded diffusion MRI, employing a voxel-wise regression neural network. Observed resilience at reduced voxel size (1.7mm)³ appears promising regarding measurement of parameters such as microscopic fractional anisotropy in a clinical setting.

Keywords: Microstructure, Microstructure

Motivation: While most biophysical models in brain white matter estimate Gaussian compartment parameters, characteristic length scales of tissue microstructure can only be obtained from non-Gaussian features.

Goal(s): To introduce time dependence into the Standard Model of diffusion; to in vivo evaluate non-Gaussian signatures of diffusion in intra- and extra-neurite spaces, irrespective of neurite orientation dispersion.

Approach: We perform diffusion measurements on the Connectome 2.0 scanner in healthy volunteers at short times (13-30 ms) and estimate time-dependent diffusion parameters using GPU-accelerated fitting.

Results: Time-dependent diffusion signals up to 2^nd-order in spherical harmonics provide sensitivity potentially related to axonal beadings and packing correlation length.

Impact: We demonstrated the feasibility of mapping time-dependent diffusion in human white matter in vivo using the Connectome 2.0 scanner. This potentially provides novel biomarkers sensitive to axon beadings and packing length scales for investigation of neurological disorders.

Keywords: Microstructure, Microstructure

Motivation: Precisely locating tumor regions is challenging for effective surgery and radiotherapy.

Goal(s): Enhance tumorous area identification accuracy.

Approach: We integrated SANDI to model both stick and spherical cell types, estimating their densities. We introduced a metric quantifying spherical cell ratio relative to total cell volume for characterizing elevated tumor cell density.

Results: Our model effectively characterizes microstructural variations in brain tumor areas. The newly proposed cell number density ratio parameter provides a distinctive marker, highlighting high cell density within tumors. This approach holds promise for improving tumor localization, treatment planning, and patient outcomes.

Impact: Brain tumor microstructure characterization using SANDI showed microstructure changes in tumors and edema. The introduced metric, sensitive to tumor changes, holds promise for early invasion detection, enhancing diagnostic precision.

Keywords: Diffusion Modeling, Microstructure

Motivation:  Magnetisation transfer (MT) and water exchange across permeable membranes operate on a similar length scale to diffusion and may impact the measured diffusion-weighted MRI (dMRI) signal. This could bias resulting parameter estimates when performing microstructural modelling.

Goal(s): To investigate how magnetisation transfer and water exchange affect dMRI measurements, particularly pore size estimations.

Approach: Monte-Carlo simulations were used to model the dMRI signal affected by MT and cross-membrane exchange in a parallel-plate geometry. Errors in plate separation estimations represent their impact on dMRI estimates. 

Results: MT had a limited effect on dMRI pore size estimation, while cross-membrane water exchange can cause large overestimations.

Impact: Pore size estimates from diffusion-weighted MRI have been found to be minimally affected by magnetisation transfer but might be significantly biased by large membrane/structure permeability. New signal models incorporating diffusion and complementary phenomena can now be explored using Monte-Carlo simulations. 

Keywords: Microstructure, Gray Matter, Exchange, relaxation, diffusion, time dependence

Motivation: It is challenging to evaluate both T₂ relaxation time and exchange time in intra-neurite and extra-cellular spaces of in vivo gray matter.

Goal(s): To evaluate neurite exchange time and compartmental T₂ relaxation times using a relaxation-diffusion-exchange protocol on Connectome 2.0.

Approach: We acquired in vivo time-dependent diffusion-relaxation MRI data with multiple diffusion times (13-30ms) and echo times (31-120ms) using the Connectome 2.0 scanner with high-performance gradient system.

Results: The exchange time and intra-neurite/extra-cellular relaxation times are about 10 ms and 120/80 ms across the cortical ribbon, respectively.

Impact: In vivo mapping of water exchange and compartmental relaxation times is achievable in the brain gray matter using the high gradient performance Connectome 2.0 scanner. Multi-contrast MR protocol helps to improve the accuracy and precision in model fitting.

Keywords: Microstructure, Diffusion/other diffusion imaging techniques

Motivation: Overcoming MRI resolution limitations is crucial for early-diagnosis of cellular-level pathologies. Achieving non-invasive MRI-images with cellular-resolution promises deeper insights into tissue-microstructure for advancing medical diagnosis.

Goal(s): We aim to obtain cellular-resolution by characterizing molecular diffusion within intra- and extra-cellular compartments. We use Non-uniform Oscillating-Gradient Spin-Echo (NOGSE) signal-filtering potential, to address this intricate molecular diffusion problem.

Approach: We employ diffusion-theory and the NOGSE-sequence, to quantitatively characterize yeast-cell phantoms mimicking tissue-microstructure.

Results: We successfully characterize distinct diffusion-regimes within and outside cells. We introduce an innovative method to discriminate intra- and extra-cellular signals without complex models and analyses, allowing selective measurements of specific tissue-microstructure compartments.

Impact: We introduce a transformative approach to non-invasive imaging, allowing cellular-level resolution discriminating different tissue-microstructure features without model assumptions. It offers a tool to investigate intricate cellular structures. Harnessing this technology might benefit patient early-diagnosis of diseases, based on quantitative images.

Keywords: Diffusion Modeling, Microstructure, water exchange, double diffusion encoding

Motivation: Microstructure imaging with diffusion MRI is substantially improved by going beyond single diffusion encoding, but the methods using such data are divergent. In particular, theories unifying b-tensor encoding and water exchange are lacking.

Goal(s): To develop and validate a theory applicable for detecting microscopic anisotropy and diffusional exchange with encoding strategies going beyond conventional diffusion MRI.

Approach: Theoretical predictions were compared with results from Monte Carlo simulations of diffusion in exchanging Gaussian environments.

Results: Theory and simulations agreed well, except when violating the assumptions of the theory. We show when and why the diffusional kurtosis decrease and when it increases due to exchange.

Impact: We identified a key condition in which exchange leads to increased signal. This condition is similar to that incorporated in neurite exchange models. Our work thus offers new experimental designs of relevance for microstructure imaging of gray matter.