Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results.

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Several magnetic resonance imaging (MRI) contrasts are sensitive to myelin content in gray matter in vivo which has ignited ambitions of MRI-based in vivo cortical histology. Ultra-high field (UHF) MRI, at fields of 7 T and beyond, is crucial to provide the resolution and contrast needed to sample contrasts over the depth of the cortex and get closer to layer resolved imaging. Ex vivo MRI of human post mortem samples is an important stepping stone to investigate MRI contrast in the cortex, validate it against histology techniques applied in situ to the same tissue, and investigate the resolutions needed to translate ex vivo findings to in vivo UHF MRI. Here, we investigate key technology to extend such UHF studies to large human brain samples while maintaining high resolution, which allows investigation of the layered architecture of several cortical areas over their entire 3D extent and their complete borders where architecture changes. A 16 channel cylindrical phased array radiofrequency (RF) receive coil was constructed to image a large post mortem occipital lobe sample (~80×80×80 mm3) in a wide-bore 9.4 T human scanner with the aim of achieving high-resolution anatomical and quantitative MR images. Compared with a human head coil at 9.4 T, the maximum Signal-to-Noise ratio (SNR) was increased by a factor of about five in the peripheral cortex. Although the transmit profile with a circularly polarized transmit mode at 9.4 T is relatively inhomogeneous over the large sample, this challenge was successfully resolved with parallel transmit using the kT-points method. Using this setup, we achieved 60μm anatomical images for the entire occipital lobe showing increased spatial definition of cortical details compared to lower resolutions. In addition, we were able to achieve sufficient control over SNR, B0 and B1 homogeneity and multi-contrast sampling to perform quantitative T2* mapping over the same volume at 200 μm. Markov Chain Monte Carlo sampling provided maximum posterior estimates of quantitative T2* and their uncertainty, allowing delineation of the stria of Gennari over the entire length and width of the calcarine sulcus. We discuss how custom RF receive coil arrays built to specific large post mortem sample sizes can provide a platform for UHF cortical layer-specific quantitative MRI over large fields of view.

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G-ratio weighted imaging is a non-invasive, in-vivo MRI-based technique that aims at estimating an aggregated measure of relative myelination of axons across the entire brain white matter. The MR g-ratio and its constituents (axonal and myelin volume fraction) are more specific to the tissue microstructure than conventional MRI metrics targeting either the myelin or axonal compartment. To calculate the MR g-ratio, an MRI-based myelin-mapping technique is combined with an axon-sensitive MR technique (such as diffusion MRI). Correction for radio-frequency transmit (B1+) field inhomogeneities is crucial for myelin mapping techniques such as magnetization transfer saturation. Here we assessed the effect of B1+ correction on g-ratio weighted imaging. To this end, the B1+ field was measured and the B1+ corrected MR g-ratio was used as the reference in a Bland-Altman analysis. We found a substantial bias (≈-89%) and error (≈37%) relative to the dynamic range of g-ratio values in the white matter if the B1+ correction was not applied. Moreover, we tested the efficiency of a data-driven B1+ correction approach that was applied retrospectively without additional reference measurements. We found that it reduced the bias and error in the MR g-ratio by a factor of three. The data-driven correction is readily available in the open-source hMRI toolbox (www.hmri.info) which is embedded in the statistical parameter mapping (SPM) framework.

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. The uncertainty in these dMRI measures is often ignored, while previous work in functional MRI has shown that incorporating uncertainty estimates can lead to group statistics with a higher statistical power. We propose the Fisher Information Matrix (FIM) as a generally applicable method for quantifying the parameter uncertainties in non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo sampling, the FIM produces similar uncertainty estimates at lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, like data complexity and signal-to-noise ratio. In individual subjects, the parameter standard deviations can help in detecting white matter artifacts as patches of relatively large standard deviations. In group statistics, we recommend using the parameter standard deviations by means of variance weighted averaging. Doing so can reduce the overall variance in group statistics and reduce the effect of data artifacts without discarding data from the analysis. Both these effects can lead to a higher statistical power in group studies.

Purpose Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion‐free spin‐ and stimulated‐echo signals and combine the signals with a physics‐driven unsupervised network to estimate T1, T2, and proton density (M0) parameter maps, along with B0 and B1 information from the acquired signals. Theory and Methods An imaging sequence with three 90° RF pulses is utilized to acquire spin‐ and stimulated‐echo signals. We utilize blip‐up/‐down acquisition to eliminate geometric distortion incurred by the effects of B0 inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo‐shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin‐ and stimulated‐echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed. Results The proposed acquisition provided distortion‐free T1, T2, relative proton density (M0), B0, and B1 maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin‐ and stimulated‐echo signals, analytic fitting and the network‐based method were able to estimate T1, T2, M0, B0, and B1 maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels. Conclusion The proposed acquisition/reconstruction technique enabled whole‐brain acquisition of coregistered, distortion‐free, T1, T2, M0, B0, and B1 maps at 1 × 1 × 5 mm3 resolution in 50 s. The proposed unsupervised neural network provided noise‐robust parameter estimates from this rapid acquisition.

The 9.4 T scanner in Maastricht is a whole-body magnet with head gradients and parallel RF transmit capability. At the time of the design, it was conceptualized to be one of the best fMRI scanners in the world, but it has also been used for anatomical and diffusion imaging. 9.4 T offers increases in sensitivity and contrast, but the technical ultra-high field (UHF) challenges, such as field inhomogeneities and constraints set by RF power deposition, are exacerbated compared to 7 T. This article reviews some of the 9.4 T work done in Maastricht. Functional imaging experiments included blood oxygenation level-dependent (BOLD) and blood-volume weighted (VASO) fMRI using different readouts. BOLD benefits from shorter T2* at 9.4 T while VASO from longer T1. We show examples of both ex vivo and in vivo anatomical imaging. For many applications, pTx and optimized coils are essential to harness the full potential of 9.4 T. Our experience shows that, while considerable effort was required compared to our 7 T scanner, we could obtain high-quality anatomical and functional data, which illustrates the potential of MR acquisitions at even higher field strengths. The practical challenges of working with a relatively unique system are also discussed.