Prediction of motion induced magnetic fields for human brain MRI at 3T

Objective Maps of B0 field inhomogeneities are often used to improve MRI image quality, even in a retrospective fashion. These field inhomogeneities depend on the exact head position within the static field but acquiring field maps (FM) at every position is time consuming. Here we explore different ways to obtain B0 predictions at different head positions. Methods FM were predicted from iterative simulations with four field factors: 1) sample induced B0 field, 2) system's spherical harmonic shim field, 3) perturbing field originating outside the field of view, 4) sequence phase errors. The simulation was improved by including local susceptibility sources estimated from UTE scans and position-specific masks. The estimation performance of the simulated FMs and a transformed FM, obtained from the measured reference FM, were compared with the actual FM at different head positions. Results The transformed FM provided inconsistent results for large head movements (>5 degree rotation), while the simulation strategy had a superior prediction accuracy for all positions. The simulated FM was used to optimize B0 shims with up to 22.2% improvement with respect to the transformed FM approach. Conclusion The proposed simulation strategy is able to predict movement induced B0 field inhomogeneities yielding more precise estimates of the ground truth field homogeneity than the transformed FM

Accelerated MRI at 9.4 T with electronically modulated time-varying receive sensitivities

PURPOSE To investigate how electronically modulated time-varying receive sensitivities can improve parallel imaging reconstruction at ultra-high field. METHODS Receive sensitivity modulation was achieved by introducing PIN diodes in the receive loops, which allow rapid switching of capacitances in both arms of each loop coil and by that alter B$_{1}$ $^{-}$ profiles, resulting in two distinct receive sensitivity configurations. A prototype 8-channel reconfigurable receive coil for human head imaging at 9.4T was built, and MR measurements were performed in both phantom and human subject. A modified SENSE reconstruction for time-varying sensitivities was formulated, and g-factor calculations were performed to investigate how modulation of receive sensitivity profiles during image encoding can improve parallel imaging reconstruction. The optimized modulation pattern was realized experimentally, and reconstructions with the time-varying sensitivities were compared with conventional static SENSE reconstructions. RESULTS The g-factor calculations showed that fast modulation of receive sensitivities in the order of the ADC dwell time during k-space acquisition can improve parallel imaging performance, as this effectively makes spatial information of both configurations simultaneously available for image encoding. This was confirmed by in vivo measurements, for which lower reconstruction errors (SSIM = 0.81 for acceleration R = 4) and g-factors (max g = 2.4; R = 4) were observed for the case of rapidly switched sensitivities compared to conventional reconstruction with static sensitivities (SSIM = 0.74 and max g = 3.2; R = 4). As the method relies on the short RF wavelength at ultra-high field, it does not yield significant benefits at 3T and below. CONCLUSIONS Time-varying receive sensitivities can be achieved by inserting PIN diodes in the receive loop coils, which allow modulation of B$_{1}$ $^{-}$ patterns. This offers an additional degree of freedom for image encoding, with the potential for improved parallel imaging performance at ultra-high field

Functional mapping of sensorimotor activation in the human thalamus at 9.4 Tesla

Although the thalamus is perceived as a passive relay station for almost all sensory signals, the function of individual thalamic nuclei remains unresolved. In the present study, we aimed to identify the sensorimotor nuclei of the thalamus in humans using task-based fMRI at a field strength of 9.4T by assessing the individual subject-specific sensorimotor BOLD response during a combined active motor (finger-tapping) and passive sensory (tactile-finger) stimulation. We demonstrate that both tasks increase BOLD signal response in the lateral nuclei group (VPL, VA, VLa, and VLp), and in the pulvinar nuclei group (PuA, PuM, and PuL). Finger-tapping stimuli evokes a stronger BOLD response compared to the tactile stimuli, and additionally engages the intralaminar nuclei group (CM and Pf). In addition, our results demonstrate reproducible thalamic nuclei activation during motor and tactile stimuli. This work provides important insight into understanding the function of individual thalamic nuclei in processing various input signals and corroborates the benefits of using ultra-high-field MR scanners for functional imaging of fine-scale deeply located brain structures

Molecular beam epitaxy of the half-Heusler antiferromagnet CuMnSb

We report growth of CuMnSb thin films by molecular beam epitaxy on InAs(001) substrates. The CuMnSb layers are compressively strained (0.6~\text{%}) due to lattice mismatch. The thin films have a $\omega$ full width half max of $7.7^{''}$ according to high resolution X-ray diffraction, and a root mean square roughness of $0.14~\text{nm}$ as determined by atomic force microscopy. Magnetic and electrical properties are found to be consistent with reported values from bulk samples. We find a N\'eel temperature of $62~\text{K}$, a Curie-Weiss temperature of $-65~\text{K}$ and an effective moment of $5.9~\mu_{\text{B}}/\text{f.u.}$. Transport measurements confirm the antiferromagetic transition and show a residual resistivity at $4~\text{K}$ of $35~\mu\Omega\cdot \text{cm}$.Comment: 6 pages, 5 figures, accepted in PR

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained