Axon diameter and myelin thickness affect the conduction velocity of action potentials in the nervous system. Imaging them non-invasively with MRI-based methods is, thus, valuable for studying brain microstructure and function. Electron microscopy studies suggest that axon diameter and myelin thickness are closely related to each other. However, the relationship between MRI-based estimates of these microstructural measures, known to be relative indices, has not been investigated across the brain mainly due to methodological limitations. In recent years, studies using ultra-high-gradient strength diffusion MRI (dMRI) have demonstrated improved estimation of axon diameter index across white-matter (WM) tracts in the human brain, making such investigations feasible. In this study, we aim to investigate relationships between tissue microstructure properties across white-matter tracts, as estimated with MRI-based methods. We collected dMRI with ultra-high-gradient strength and multi-echo spin-echo MRI on ex vivo macaque and human brain samples on a preclinical scanner. From these data, we estimated axon diameter index, intra-axonal signal fraction, myelin water fraction (MWF), and aggregate g-ratio and investigated their correlations. We found that the correlations between axon diameter index and other microstructural imaging parameters were weak but consistent across WM tracts in samples estimated with sufficient signal-to-noise ratio. In well-myelinated regions, tissue voxels with larger axon diameter indices were associated with lower packing density, lower MWF, and a tendency of higher g-ratio. We also found that intra-axonal signal fractions and MWF were not consistently correlated when assessed in different samples. Overall, the findings suggest that MRI-based axon geometry and myelination measures can provide complementary information about fiber morphology, and the relationships between these measures agree with prior electron microscopy studies in smaller field of views. Combining these advanced measures to characterize tissue morphology may help differentiate tissue changes during disease processes such as demyelination versus axonal damage. The regional variations and relationships of microstructural measures in control samples as reported in this study may serve as a point of reference for investigating such tissue changes in disease.

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.

The primary aim of this study is to address the challenges in submillimeter diffusion magnetic resonance imaging (dMRI), such as prolonged acquisition time, low signal-to-noise ratio (SNR), and signal attenuation at slab boundary. We introduce a novel 3D Fourier encoding mechanism, PRISM (Partition-encoded Simultaneous Multislab), and a new concept termed “pseudo slab.” The PRISM method allows simultaneous inter-slab and intra-slab Fourier encoding solely using the slice gradient, eliminating the need for RF encoding. The pseudo slab concept not only minimizes inter-slab signal leakage and Gibbs truncation artifacts, but also enables phase scheduling onto intra-slab slices, thus eliminating the need for a phase navigator and time-varying gradient such as variable-rate selective excitation (VERSE). Integrating the pseudo slab with PRISM, the resulting pseudo PRISM (pPRISM) technique achieved rapid acquisition of dMRI with 0.86-mm isotropic resolution and an effective TR of 12 s (TR of 2.4 s per shot). Compared to Generalized Slice Dithered Enhanced Resolution with Simultaneous Multislice (gSlider-SMS), the shortened acquisition time improved the SNR efficiency without aggravating the signal attenuation at slab boundaries. The robustness of pPRISM against field inhomogeneity was also supported by Bloch simulation and empirical data. Furthermore, dMRI was successfully achieved with a 0.76-mm isotropic resolution, an effective TR of 15 s, and b-values of up to 2500 s/mm 2 . The ultrahigh-resolution results of the proposed pPRISM method demonstrated the anticipated dark bands of fractional anisotropy (FA) at gray-white matter boundaries and yielded more plausible tractography results. Our pPRISM framework paves the way for acquiring ultrahigh-resolution dMRI in clinically feasible times, advancing microstructural research.

Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo microscope. In brain white matter, the Standard Model (SM) interprets the dMRI signal in terms of axon dispersion, intra- and extra-axonal water fractions, and diffusivities. However, for SM to be fully applicable and correctly interpreted, it needs to be carefully evaluated using histology. Here, we perform a comprehensive histological validation of the SM parameters, by characterizing white matter (WM) microstructure in sham and injured rat brains using volume electron microscopy and ex vivo dMRI. Sensitivity is evaluated by how well each SM metric correlates with its histological counterpart, and specificity by the lack of correlation with other, non-corresponding histological features. Compared to previously developed SM estimators with constraints, our results show that SMI is the most sensitive and specific. Furthermore, we derive the functional form of the fiber orientation distribution based on its exponentially decreasing rotational invariants. This comprehensive comparison with histology may facilitate the clinical adoption of in vivo dMRI-derived SM parameters as biomarkers for neurological disorders.

Various diffusion MRI (dMRI) preprocessing pipelines are currently available to yield more accurate diffusion parameters. Here, we evaluated accuracy and robustness of the optimized Diffusion parameter EStImation with Gibbs and NoisE Removal (DESIGNER) pipeline in a large clinical dMRI dataset and using ground-truth phantoms. DESIGNER, a preprocessing pipeline targeting various imaging artifacts in diffusion MRI data, has been modified to improve denoising and target Gibbs ringing for partial Fourier acquisitions. We compared the revised DESIGNER (Dv2) (including denoising, Gibbs removal, correction for motion, echo planar imaging (EPI) distortion, and eddy currents) against the original DESIGNER (Dv1) pipeline, minimal preprocessing (including correction for motion, EPI distortion, and eddy currents only), and no preprocessing on a large clinical dMRI dataset of 524 control subjects with ages between 25 and 75 years old. We evaluated the effect of specific processing steps on age correlations in white matter with diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) metrics. We also evaluated the added effect of minimal Gaussian smoothing to deal with noise and to reduce outliers in parameter maps compared to DESIGNER-v2’s noise removal method. Moreover, Dv2’s updated noise and Gibbs removal methods were assessed using a ground truth dMRI phantom to evaluate accuracy. Results show age correlations of DTI and DKI metrics in white matter were affected by the preprocessing pipeline, causing systematic differences in absolute parameter values and loss or gain of statistical significance. Both in clinical dMRI and ground-truth phantoms, Dv2 pipeline resulted in the smallest number of outlier voxels and improved accuracy in DTI and DKI metrics as noise was reduced and Gibbs removal was improved. Thus, DESIGNER-v2 provides more accurate and robust DTI and DKI parameter maps by targeting common artifacts present in dMRI data acquired in clinical settings, as compared to no preprocessing or minimal preprocessing.