Keywords: Microstructure, Microstructure, Diffusion

Motivation: We wish to quantify the amount of axons in the brain using clinically-feasible in vivo human diffusion MRI.

Goal(s): We want to estimate the axonal signal fraction using the conventional pulsed gradient spin echo (PGSE) sequence while reducing model degeneracy and minimizing modeling assumptions.

Approach: We model spherical harmonic (SH) coefficients across two high b-value PGSE shells. We calculate ratios between SH l-band power spectra across the shells, relate them analytically to the axonal diffusivities - estimated using machine learning - and with these we calculate the axonal signal fraction.

Results: We report comparable results across preclinical and clinical data and demonstrate methodological feasibility.

Impact: The axonal signal fraction is proportional to the total volume of axons within a voxel and can be used to characterize pathology. This work proposes its estimation with clinically-feasible b-values and with conventional diffusion MRI data while minimizing modeling assumptions.

Keywords: Gray Matter, Diffusion/other diffusion imaging techniques, Gray matter, High-performance gradient system

Motivation: The Connectome 2.0 MRI scanner equipped with 500 mT/m gradient strength and 600 T/m/s slew rate is expected to advance gray matter microstructural characterization within the living human brain.

Goal(s): To compare Soma And Neurite Density imaging (SANDI) metrics obtained from Connectome 2.0 and 1.0.

Approach: We applied SANDI model fitting to diffusion MRI data acquired from 10 healthy subjects scanned on the Connectome 2.0 and 1.0 scanners.

Results: We observed lower soma radius throughout the cortex on Connectome 2.0 compared to Connectome 1.0. SANDI metrics from Connectome 2.0 exhibited considerable contrast within the sensorimotor cortex that wasn’t apparent on Connectome 1.0.

Impact: The Connectome 2.0 MRI scanner with 500 mT/m gradients advances non-invasive characterization of gray matter microstructure in the living human brain and enables mapping of real differences in cyto- and myeloarchitecture with greater sensitivity compared to the original Connectome scanner.

Keywords: Microstructure, Diffusion/other diffusion imaging techniques

Motivation: We address the challenge of characterizing complex diffusion spectra within biological tissues, with implications for early disease detection.

Goal(s): We aimed to employ OGSE sequences to probe water diffusion in phantom models, separating intra- and extracellular diffusion components.

Approach: Using OGSE sequences, we varied diffusion times and gradient values, effectively disentangling diffusion spectral densities and analyzing restricted diffusion patterns.

Results: We successfully separated intra- and extra-cellular diffusion spectral densities. The intra-cellular one provides the cell-sizes. In contrast, the extra-cellular spectral densities exhibited complexity, necessitating the development of models for interpretation. These insights  hold promise for early disease diagnosis based on tissue-microstructure characterization.

Impact: Our study successfully separates intra- and extracellular diffusion spectral densities, enabling cell-size determination, advancing medical imaging. It fosters nuanced tissue microstructure characterization, opening new avenues for early disease diagnosis and innovative clinical applications.

Keywords: Microstructure, Brain, Spatial Transcriptomics

Motivation: Characterizing developmental brain microstructure changes is important for understanding the mechanism of brain development at cellular level.

Goal(s): We aimed to study brain microstructure and to correlate phenotypical diffusivity variations with genotypic expression profiles.

Approach: We imaged postnatal mouse brains by high-resolution diffusion magnetic resonance imaging (dMRI) with both DTI and NODDI models to extract quantitative diffusion metrics. dMRI-gene expression correlation was tested by regression model.

Results: Distinct growth patterns are observed by quantitative dMRI parameters in white matter bundles, isocortex, hippocampus, and cerebellum. Genes related to nerve system displayed unique spatial and temporal expression patterns corresponding with dMRI alternations during brain development.

Impact: This study may improve our understanding of brain microstructure changes during postnatal development at molecular and cellular level. This study also provides non-invasive imaging techniques to quantitatively investigate neurodevelopmental disorders at high resolution.

Keywords: Microstructure, Microstructure

Motivation: Multidimensional (MD)-MRI provides valuable subvoxel information. However, its experimental variability has never been investigated.

Goal(s): Assessing the variability of MD-MRI estimates is essential for adaptation to clinical research and widespread use.

Approach: Ten healthy participants were each scanned twice, using a 40-minute 2-mm3 resolution diffusion-relaxation MD-MRI protocol. Agreement, reliability, and repeatability were assessed using Bland-Altman plots, intraclass correlation coefficient, and test-retest variability, respectively.

Results: We demonstrated a good to excellent reliability in the quantification of first- and second-order diffusion parameters. Our findings guide further improvement to the protocol and encourage the use of this MD-MRI framework in clinical research.

Impact: Multidimensional MRI is crucial for investigating tissue microstructure, brain connectivity, and pathology. Here we present an in vivo variability study that shows strong agreement, reliability, and repeatability, especially for diffusion parameters, providing a way forward for clinical research.

Keywords: Microstructure, Diffusion/other diffusion imaging techniques, Multidimensional Diffusion

Motivation: The reproducibility and sensitivity of Multidimensional Diffusion (MDD) measures have not been extensively investigated.

Goal(s): Our goal was to assess reproducibility and sensitivity of µFA in comparison with conventional FA metrics for multiple white and grey matter regions.

Approach: Test-retest data was acquired to compute ICC scores (reproducibility) and power calculations (sensitivity) to predict the number of subjects required to detect 1%, 2%, 4% and 5% changes for each metric across multiple regions.

Results: While FA shows the highest reproducibility for both WM and GM, µFA demonstrates enhanced sensitivity to detect microstructural changes for the same percentage difference.

Impact: Our test-retest study demonstrates that Multidimensional Diffusion (MDD) metrics, like µFA, show good reproducibility scores and increased sensitivity in the detection of microstructural changes when compared to equivalent FA changes in both white and grey matter.

Keywords: Simulation/Validation, Diffusion/other diffusion imaging techniques

Motivation: Intraparenchymal brain infusions using convection enhanced delivery (CED) require predictive modeling to plan catheter locations to visualize end distributions. Currently, diffusion MRI indirectly estimates extracellular volume fraction (ECVF) and creates hydraulic conductivity maps.

Goal(s): To directly measure ECVF parameters using advanced biophysical diffusion models.

Approach: Quantitative brain maps of ECVF from a pre-existing database were generated with diffusion model SANDI. These were compared with prior ECVF estimates determined via invasive physiological techniques.

Results: SANDI modeling predicted ECVF ranges of 0.14-0.26 in white matter across 12 cases, consistent with consensus values around 0.20 estimated from physiology.

Impact: SANDI can quantitatively measure extracellular volume fractions in white matter. Previous methods estimated these parameters indirectly. Direct parametrization could be used for more accurate estimates of end-state biologic distributions in proposed trials of monogenic pediatric neurodegenerative gene therapy trials.

Keywords: Diffusion Acquisition, Image Reconstruction, 3D multi-slab imaging, Submillimeter, Diffusion acquisition, Diffusion reconstruction

Motivation: Submillimeter diffusion MRI is desirable for neuroscientific research but suffers from intrinsically low SNR.

Goal(s): To achieve submillimeter isotropic resolution in-vivo diffusion MRI with superior SNR and minimal blurring and distortion.

Approach: We quantify the effective resolution and SNR for high-resolution diffusion-weighted EPI, based on which a 3D Fourier encoding-based acquisition with in-plane segmented multi-slab EPI at 7T and a denoiser-regularized reconstruction framework are developed.

Results: In-vivo whole-brain experiments at 0.61 mm isotropic resolution reveal more detailed microstructure compared to 1.05 mm data, which also demonstrates great consistency with a previous post-mortem study. The acquired data also exhibit great anatomical fidelity.

Impact: Our study achieves 3D Fourier-encoded 0.61 mm isotropic resolution whole-brain in-vivo diffusion MRI with minimal blurring and distortion and superior SNR, showing great potential to reveal more detailed depiction of microstructure of the living human brain and advance neuroscientific research.

Keywords: Microstructure, Microstructure, axon radius, membrane permeability, development, postnatal

Motivation: Axon and soma radii and membrane permeability are important features of brain structure and pathology but in-vivo noninvasive measurement of these biomarkers is challenging due to complex tissue architectures and inherent simplifying assumptions in MR biophysical models. 

Goal(s): We study membrane permeability and axon and soma radii of the developing brain during the first 5 years of life, the most dynamic and complex postnatal neurodevelopment.

Approach: Different microstructure models were fitted to 389 diffusion MRI scans of 217 subjects. Measurements and developmental trends were compared with histological evidence.

Results: MF-SMSI yields results that are closest to histological expectations with biologically plausible developmental trends. 

Impact: Noninvasive measurements of cell physical properties in the developing brain were compared with histological evidence. We present a method that yields results that are closest to histological expectations with biologically plausible developmental trends. The method can potentially facilitate neurodevelopmental studies.

Keywords: Diffusion Modeling, Microstructure, relaxometry; multi-dimensional; stroke; ischaemia; pre-clinical

Motivation: The applicability of NODDI and its spinoffs, e.g. multi-echo (MTE) NODDI, is limited to those tissue conditions where the intrinsic diffusivity is known a priori.

Goal(s): We propose an estimation approach for MTE-NODDI parameters in which the intrinsic diffusivity is released whilst ensuring fitting stability by adding an l₂-norm regularisation term to the cost function.

Approach: The regularisation parameter was optimised via the generalised cross-validation approach. The MTE-NODDI with released intrinsic diffusivity was tested in a healthy rat at 3 T.

Results: The estimation of MTE-NODDI with released intrinsic diffusivity is well-conditioned if an l₂-norm regularisation term is used.

Impact: The proposed estimation approach for MTE-NODDI parameters with released intrinsic diffusivity was shown to be stable. Thus, a new range of tissue conditions in which the intrinsic diffusivity is known to be affected, e.g. stroke, can be accurately characterised.

Keywords: Diffusion Modeling, Microstructure, NODDI model, time stability and consistency, single centre

Motivation: The NODDI diffusion-MRI model shows promising results in characterising brain microstructure and capturing neurological disease-related changes. However, the NODDI model lacks validation, limiting its clinical application.

Goal(s): The goal is to validate the diffusion MRI NODDI model, assessing its consistency over time and addressing the need for robust methods in clinical research.

Approach: By scanning several times phantoms simulating brain-restricted diffusion and healthy volunteers with the same acquisition protocol, we meticulously assess NODDI's stability over time and in the presence of magnetic gradient coil heating.

Results: The study confirms the NODDI model's exceptional consistency and stability, establishing its credibility for future clinical applications.

Impact: The study confirms the reliability and stability of the NODDI model in assessing brain microstructure over time. This has significant implications for monitoring neurological disease progression and may lead to standardised MRI calibration protocols for collaborative research and clinical applications.

Keywords: Diffusion Modeling, Microstructure, Multicenter study, multicenter phantom study, multicenter stability assessment of Bingham-NODDI model, Bingham-NODDI model, NODDI model

Motivation: The NODDI model has been proven to be a powerful tool, however it struggles to depict complex brain neurite structures.
 
 
 

Goal(s): The Bingham-NODDI model offers a more detailed approach for capturing pathological-related brain changes, although its validation is still ongoing. This study aims to make progress in this direction.

Approach: To assess the Bingham-NODDI model's reliability across different MRI scanner systems, this study uses a consistent acquisition protocol, fostering the model's robustness.

Results: The outcomes confirm Bingham-NODDI model’s reliability, promising potential clinical applications. By enhancing generalizability and validation, this research clears the path for improved diagnostic and research tools in neuroscience. 
 
 
 

Impact: The Bingham-NODDI model offers valuable insights into brain microstructure changes. Its integration into clinical practice alongside medical teams and neuroscientists is highly promising. This multi-center study advances this goal by assessing model stability, fostering future collaborations between scientistis and clinicians. 

Keywords: Diffusion Modeling, Diffusion/other diffusion imaging techniques, Rician Bias, Noise, SMI

Motivation: Rician noise degrades the accuracy of biophysical modeling. Understanding this bias and defining a robust strategy for its mitigation, is important for reproducible and quantitative use of diffusion MRI.

Goal(s): To study the impact of noise biases on biophysical models and evaluate methods for more accurate estimation of diffusion metrics.

Approach: We compare various parameter estimators (via simulations and MRI data) and evaluate their impact on the accuracy of biophysical model parameters.

Results: The use of rotational-invariant spherical harmonics in biophysical modeling is a source of noise bias that can be mitigated by fitting such models directly to the diffusion-weighted signals. 

Impact: With the advent of higher b-values, Rician bias pose a threat to the reproducibility in diffusion MRI studies. With this work we take a deep look at such bias and propose alternatives to avoid such counfounders from the final estimates. 

Keywords: Microstructure, Brain, low-field high-gradient

Motivation: To understand the mechanisms of water exchange.

Goal(s): To determine the extent that DEXSY-based water exchange measurements depend on changes to tissue microstructure (e.g., cellular swelling/shrinking).

Approach: NMR measurements of ADC, exchange rate and light microscopy measurements of intrinsic optical signal (IOS) are acquired simultaneously in real time on viable and dead ex vivo neural tissue during potassium and osmotic perturbations.

Results: In dead samples, exchange, ADC, and IOS are similarly affected. In viable samples, however, some perturbations affect exchange differently than they do the ADC and IOS that reflect changes in microsctructure alone. Exchange may be physiologically regulated independently of cell volume.

Impact: Water exchange is sensitive to cellular processes distinct from microstructure and blood flow. This measurement possibly could lead to a new fMRI method.

Keywords: Simulation/Validation, Validation, Brownian Motion, Fractional Brownian Motion, Super-Statistics, dynamics, Polymer solution

Motivation: Currently, diffusion models are applied without knowing the type of dynamics a priori. The risk is to quantify parameters that do not reflect the dynamics of the system. 

Goal(s): Here we show that it is possible to carry out a preliminary check to know the type of dynamics a priori. 

Approach: We tested our recipe in 40% w/w Poly(ethylene glycol) (PEG) water-solution using PGSTE at different big Delta, little Delta and gradient strength. 

Results: We showed that both water and PEG CH₂ showed a Super-Statistic dynamic while the dynamics of the OH tail of PEG is an unknown process.

Impact: Currently, diffusion models are applied without knowing the type of dynamics a priori. We introduce a recipe to know the type of dynamics. This method avoids the quantification of diffusion-parameters that do not reflect the true diffusion of the system.

Keywords: Simulation/Validation, Signal Representations, sub-diffusion model, brain cell morphology

Motivation: The diffusion MRI signal in brain tissues can be modelled as a sub-diffusion process. The connection between sub-diffusion model parameters and microstructure of brain cells is yet to be explored.

Goal(s): The research aims to investigate the link between sub-diffusion model parameters and brain cell morphometrics. 

Approach: Monte Carlo simulations are performed for representative brain cell types. The sub-diffusion model are then fitted to the simulated diffusion MRI data for each cell type.

Results: Results reveal that the sub-diffusion model parameters are sensitive to the branch order of the cell, with higher parameter values indicating higher branch order.

Impact: This is the first study to investigate how the sub-diffusion model parameters link to brain cell morphometrics. Our findings may provide new opportunities in diffusion MRI, where cell morphology and potentially cell type are of interest.