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Purpose: Universal Pulses (UPs) are excitation pulses that reduce the flip angle inhomogeneity in high field MRI systems without subject-specific optimization, originally developed for parallel transmit (PTX) systems at 7T. We investigated the potential benefits of UPs for single channel (SC) transmit systems at 3T, which are widely used for clinical and research imaging, and for which flip angle inhomogeneity can still be problematic. Methods: SC-UPs were designed using a spiral nonselective k-space trajectory for brain imaging at 3T using transmit field maps (B1+) and off-resonance maps (B0) acquired on two different scanner types: a 'standard' single channel transmit system and a system with a PTX body coil. The effect of training group size was investigated using data (200 subjects) from the standard system. The PTX system was used to compare SC-UPs to PTX-UPs (15 subjects). In two additional subjects, prospective imaging using SC-UP was studied. Results: Average flip angle error fell from 9.5+/-0.5% for 'default' excitation to 3.0+/-0.6% using SC-UPs trained over 50 subjects. Performance of the UPs was found to steadily improve as training group size increased, but stabilized after ~15 subjects. On the PTX-enabled system, SC-UPs again outperformed default excitation in simulations (4.8+/-0.6% error versus 10.6+/-0.8% respectively) though greater homogenization could be achieved with PTX-UPs (3.9+/-0.6%) and personalized pulses (SC-PP 3.6+/-1.0%, PTX-PP 2.9+/-0.6%). MP-RAGE imaging using SC-UP resulted in greater separation between grey and white matter signal intensities than default excitation. Conclusions: SC-UPs can improve excitation homogeneity in standard 3T systems without further calibration and could be used instead of a default excitation pulse for nonselective neuroimaging at 3T., Comment: Submitted to Magnetic Resonance Imaging

Purpose: Inter-scan motion is a substantial source of error in $R_1$ estimation, and can be expected to increase at 7T where $B_1$ fields are more inhomogeneous. The established correction scheme does not translate to 7T since it requires a body coil reference. Here we introduce two alternatives that outperform the established method. Since they compute relative sensitivities they do not require body coil images. Theory: The proposed methods use coil-combined magnitude images to obtain the relative coil sensitivities. The first method efficiently computes the relative sensitivities via a simple ratio; the second by fitting a more sophisticated generative model. Methods: $R_1$ maps were computed using the variable flip angle (VFA) approach. Multiple datasets were acquired at 3T and 7T, with and without motion between the acquisition of the VFA volumes. $R_1$ maps were constructed without correction, with the proposed corrections, and (at 3T) with the previously established correction scheme. Results: At 3T, the proposed methods outperform the baseline method. Inter-scan motion artefacts were also reduced at 7T. However, reproducibility only converged on that of the no motion condition if position-specific transmit field effects were also incorporated. Conclusion: The proposed methods simplify inter-scan motion correction of $R_1$ maps and are applicable at both 3T and 7T, where a body coil is typically not available. The open-source code for all methods is made publicly available.

Quantitative MR imaging is increasingly favoured for its richer information content and standardised measures. However, computing quantitative parameter maps, such as those encoding longitudinal relaxation rate (R1), apparent transverse relaxation rate (R2*) or magnetisation-transfer saturation (MTsat), involves inverting a highly non-linear function. Many methods for deriving parameter maps assume perfect measurements and do not consider how noise is propagated through the estimation procedure, resulting in needlessly noisy maps. Instead, we propose a probabilistic generative (forward) model of the entire dataset, which is formulated and inverted to jointly recover (log) parameter maps with a well-defined probabilistic interpretation (e.g., maximum likelihood or maximum a posteriori). The second order optimisation we propose for model fitting achieves rapid and stable convergence thanks to a novel approximate Hessian. We demonstrate the utility of our flexible framework in the context of recovering more accurate maps from data acquired using the popular multi-parameter mapping protocol. We also show how to incorporate a joint total variation prior to further decrease the noise in the maps, noting that the probabilistic formulation allows the uncertainty on the recovered parameter maps to be estimated. Our implementation uses a PyTorch backend and benefits from GPU acceleration. It is available at https://github.com/balbasty/nitorch., Comment: 20 pages, 6 figures, accepted at Medical Image Analysis

Quantitative magnetic resonance imaging (qMRI) derives tissue-specific parameters -- such as the apparent transverse relaxation rate R2*, the longitudinal relaxation rate R1 and the magnetisation transfer saturation -- that can be compared across sites and scanners and carry important information about the underlying microstructure. The multi-parameter mapping (MPM) protocol takes advantage of multi-echo acquisitions with variable flip angles to extract these parameters in a clinically acceptable scan time. In this context, ESTATICS performs a joint loglinear fit of multiple echo series to extract R2* and multiple extrapolated intercepts, thereby improving robustness to motion and decreasing the variance of the estimators. In this paper, we extend this model in two ways: (1) by introducing a joint total variation (JTV) prior on the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a posteriori} estimate. We evaluated the proposed algorithm by predicting left-out echoes in a rich single-subject dataset. In this validation, we outperformed other state-of-the-art methods and additionally showed that the proposed approach greatly reduces the variance of the estimated maps, without introducing bias., Comment: 11 pages, 2 figures, 1 table, conference paper, accepted at MICCAI 2020

The g-ratio, quantifying the comparative thickness of the myelin sheath encasing an axon, is a geometrical invariant that has high functional relevance because of its importance in determining neuronal conduction velocity. Advances in MRI data acquisition and signal modelling have put in vivo mapping of the g-ratio, across the entire white matter, within our reach. This capacity would greatly increase our knowledge of the nervous system: how it functions, and how it is impacted by disease. This is the second review on the topic of g-ratio mapping using MRI. As such, it summarizes the most recent developments in the field, while also providing methodological background pertinent to aggregate g-ratio weighted mapping, and discussing pitfalls associated with these approaches. Using simulations based on recently published data, this review demonstrates the relevance of the calibration step for three myelin-markers (macromolecular tissue volume, myelin water fraction, and bound pool fraction). It highlights the need to estimate both the slope and offset of the relationship between these MRI-based markers and the true myelin volume fraction if we are really to achieve the goal of precise, high sensitivity g-ratio mapping in vivo. Other challenges discussed in this review further evidence the need for gold standard measurements of human brain tissue from ex vivo histology. We conclude that the quest to find the most appropriate MRI biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of many novel techniques yet to be investigated., Comment: Will be published as a review article in Journal of Neuroscience Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest Editors

In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol - and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison, a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are inferred using Dynamic Casual Modelling (DCM) - a commonly used Bayesian procedure for modelling neuroimaging data. Next, the parameters from subject-specific models are summarised at the group level using a Bayesian General Linear Model (GLM). A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software., Comment: Equal contribution by Peter Zeidman and Samira M Kazan