Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength = 500 mT/m, maximum slew rate = 600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13–30 ms) and b -values up to 17.5 ms/μm 2 . The anisotropic Kärger model was applied to estimate the apparent exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median ± interquartile range) 13 ± 8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the apparent exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast (≤20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex, and visual cortex exhibit longer apparent exchange times than other cortical regions. Non-linear fitting for the anisotropic Kärger model was accelerated 100 times using a GPU-based pipeline compared with the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in diffusion MRI (dMRI) data, which can have a substantial impact on the estimated apparent exchange time and require extra attention when comparing the results between various hardware setups.

Quantitative MRI (qMRI) techniques, including R 1 , R 2 *, and magnetic susceptibility mapping, have emerged as promising tools for generating surrogate imaging markers of brain tissue microstructure, enabling non-invasive in vivo measurements associated with myelination and iron deposition. Gaining insights into how these quantitative measurements evolve throughout a normal lifespan can enhance our understanding of brain maturation processes and facilitate studies of disease-related microstructural changes by distinguishing pathological alterations from normal brain development. In this study, we established the normative trajectories of R 1 , R 2 *, and magnetic susceptibility in the basal ganglia at 3T. We used a healthy ageing cohort comprising 260 subjects with an evenly distributed age range (from 18 to 80 years) and sex ratio throughout adulthood. Utilising the non-parametric Gaussian Process Regression model to derive the normative trajectories, we found that R 1 in these structures predominantly exhibits a quadratic shape over age, while R 2 * and magnetic susceptibility are primarily linear. We validated the normative trajectories of R 2 * and magnetic susceptibility using an independent cohort. This result reinforces existing findings on the association between age and qMRI. Additionally, we demonstrated that the spatial distributions of the qMRI parameters also change with age in the putamen and caudate nucleus. Finally, the utility of normative modelling of qMRI in the basal ganglia is validated using an independent cohort comprising both healthy participants and individuals with Parkinson’s disease, with comparable data acquisition protocols.