Keywords: Diffusion Modeling, Diffusion/other diffusion imaging techniques

Motivation: The advent of quantitative imaging hinges on revealing the information content of MRI measurements. We provide a complete basis- and hardware-independent “fingerprint" for the diffusion signal up to moderate diffusion-weightings.

Goal(s): Find all rotationally invariant information present in the cumulant expansion up to b².

Approach: We classify all invariants of diffusion and covariance tensors in terms of irreducible representations of the group of rotations, discuss their geometric meaning, and relate them to tissue properties.

Results: We find a complete set of 21 independent rotational invariants up to b². Previously studied contrasts are expressed via only 7, while the rest provide novel complementary information.

Impact: We map the diffusion covariance tensor onto the addition of angular momenta, and provide all rotational invariants of the cumulant expansion (RICE). RICE apply to >50k publicly available human diffusion MRI datasets, providing new insights into tissue properties.

Keywords: Diffusion Analysis & Visualization, Data Analysis, Multidimensional MRI

Motivation: Multidimensional diffusion-relaxation MRI opened new ways to non-invasively study sub-voxel populations of water with distinct MRI signal responses and, by inference, tissue microstructure. However, this technique creates large number nonparametric diffusion-relaxation distributions that are challenging to visualize or translate into microstructure specific maps.

Goal(s): The goal of this study is to automatically classify the distribution components for an ex vivo rat brain and compare them with histology to reveal their links to tissue fractions.

Approach:  To achieve the automatic classification, we use an unsupervised data-driven clustering approach.

Results: We successfully separated white matter, gray matter, free water and additional tissue fractions.

Impact: Multidimensional diffusion-relaxation MRI combined with data-driven microstructure clustering offers new perspectives in high-specificity studies of healthy and damage tissue beyond the conventional white matter, gray matter, and free-water fractions. This is achieved by exploring the full sub-voxel multidimensional distribution space.

Keywords: Diffusion Modeling, Diffusion/other diffusion imaging techniques, intra-axonal T2, spherical variance, Rician bias

Motivation: The spherical variance (SV) from multiparametric diffusion MRI acquisitions enables the estimation of the axonal T2 relaxation time. The SV is prone to a noise-induced bias due to positively skewed Rician statistics, leading to overestimation in the axonal T2 parameter.

Goal(s): To derive a method to mitigate the Rician bias in the SV parameter map.

Approach: A closed-form formula to remove the Rician bias from the SV has been analytically derived and verified with in silico and in vivo data.

Results: The bias-corrected SV reduces the estimation error compared to the SV, translating to a less pronounced misestimation in the axonal T2 parameter.

Impact: The SV is a practical parameter to infer the properties of restricted compartments with diffusion MRI. This work shows a formula to remove the Rician bias from the SV. The correction can be used for other SV-based diffusion MRI measures.

Keywords: Microstructure, Microstructure

Motivation: In vivo mapping of exchange between intra-neurite and extracellular water in gray matter is challenging, as the required strong diffusion weighting significantly reduces the signal-to-noise ratio. 

Goal(s): We aim to demonstrate the feasibility of in vivo neurite exchange imaging using the state-of-the-art Connectome 2.0 scanner equipped with high-performance gradient system (G_max=500mT/m, (dG/dt)_max=600T/m/s).

Approach: We acquired in vivo diffusion MRI measurements with multiple diffusion times (13-30ms) up to b-values of 17.5ms/μm² on 5 human subjects. Anisotropic Kärger model (NEXI/SMEX) was used to estimate the exchange time from the diffusion data.

Results: The exchange time across the cortical ribbon is around 16ms.

Impact: The high performance gradient system on the Connectome 2.0 scanner enables in vivo mapping of water exchange time in gray matter, providing a tool to study neurite permeability in the healthy human brain and a variety of neuropsychiatric disorders.

Keywords: Microstructure, Brain, DDE, metabolites, microstructure

Motivation: Double-Diffusion-Encoded Spectroscopy (DDES) provides multiple metrics of cell-specific morphology in a single MR experiment but is prone to motion-induced signal distortions.

Goal(s): To obtain robust microstructural metrics of cell-type specific diffusion in different brain regions.

Approach: We combine DDES with metabolite-cycling (MC) and motion-compensation (MoCom) to correct for signal distortions in post processing.

Results: MoCom improves DDES data quality and reproducibility and allows metabolite specific diffusion metrics to be obtained on clinical 3T MR scanners.

Impact: The implementation of robust Double-Diffusion-Encoded Spectroscopy (DDES) on clinical MR scanners can shed new light on cellular microstructure in the healthy and pathological brain.

Keywords: Microstructure, Diffusion/other diffusion imaging techniques, Brain Maturation, Tensor-valued diffusion MRI

Motivation: Healthy rodent brain maturation research remains limited, although rodents are a predominant study model, which motivates further study to exclude confounds of developmental changes from pathophysiological interpretations.

Goal(s): Our goals were to investigate how microstructural MRI metrics change over the course of brain maturation and disentangle what changes in these metrics may indicate on a neurobiological level.

Approach: 11 mice were scanned at 9.4T between 3-8 months of age, with histology (n=4) performed at 3 and 8 months.

Results: Total diffusional kurtosis and myelin-specific metrics showed significant increases over time, paired with increased isotropic kurtosis and increased histological oligodendrocyte and astrocyte content.

Impact: This work shows that there are ongoing microstructural changes even after mice are considered “adults”, detectable by isotropic kurtosis. We provide new interpretations of diffusion MRI changes during brain maturation, with evidence of the underlying mechanisms impacting isotropic kurtosis.

Keywords: IVIM, Perfusion, IVIM, Diffusion

Motivation: Lack of consensus in perfusion modelling in IVIM MRI, with focus on blood flow in biological tissue.

Goal(s): To model perfusion in flow-compensated IVIM MRI to allow for blood velocity and correlation time quantification.

Approach: Using a velocity autocorrelation function to describe the dynamics of capillary blood flow, an expression for perfusion signal attenuation was derived for flow-compensated IVIM MRI, and evaluated in healthy brain.

Results: The proposed model allows for direct quantification of velocity and correlation time of blood flow, in addition to perfusion fraction and diffusion coefficient. 

Impact: The study presents initial proof-of-concept directly quantifying velocity and correlation time of blood flow in healthy brain using flow-compensated Intravoxel Incoherent Motion MRI. Access to these parameters can assist in characterizing tissue microvasculature in disease.

Keywords: IVIM, Brain

Motivation: The assumption of Gaussian diffusion in the extravascular space in the IVIM model does not necessarily hold, especially for neuronal tissues.

Goal(s): To mitigate the impacts on IVIM estimation from complicated extravascular space diffusion such as in the crossing-fiber tissues.

Approach: We employed Spherical Tensor Encoding (STE) in place of the Linear Tensor Encoding (LTE) as in the conventional Stejskal-Tanner experiment to eliminate the orientational dependence of diffusion signal.

Results: The feasibility of the microscopic IVIM based on STE experiments was demonstrated in both healthy and diseased participants, with expected contrasts according to known anatomy/pathology.

Impact: A new framework of IVIM measurement was proposed based on the Linear Tensor Encoding diffusion experiment. The proposed approach can achieve diffusivity estimates in one excitation, which will otherwise require acquisition of multiple diffusion weighting directions.  

Keywords: Microstructure, Modelling, Immunotherapy, Liver, Tumours, Histology

Motivation: Multi-compartment liver diffusion MRI (dMRI) provides innovative markers of intra-cellular fraction (F) and cell size (CS). However, practical implementations for histologically-meaningful F and CS computation in the clinic are still sought.

Goal(s): To deliver a compact approach for F and CS estimation, informing model design with histology.

Approach: We compared 5 implementations of a standard two-compartment model for their ability to provide F and CS estimates that agree with reference biopsies in liver tumours.

Results: The best approach consisted of fitting a single-compartment model of intra-cellular diffusion to high b-value images. This provides promising metrics that stratify the risk of progression in immunotherapy.

Impact: We deliver a clinically-feasible liver diffusion MRI approach for intra-cellular fraction, cell size and density estimation. It consists of fitting a single-compartment model of restricted diffusion to high b-value images, and provides metrics that may inform on cancer immunotherapy response.