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Purpose: Clinical use of transcranial electrical stimulation (TES) requires accurate knowledge of the injected current distribution in the brain. MR current density imaging (MRCDI) uses measurements of the TES-induced magnetic fields to provide this information. However, sufficient sensitivity and image quality in humans in vivo has only been documented for single-slice imaging.Methods: A recently developed, optimally spoiled, acquisition-weighted, gradient echo-based 2D-MRCDI method has now been advanced for volume coverage with densely or sparsely distributed slices: The 3D rectilinear sampling (3D-DENSE) and simultaneous multislice acquisition (SMS-SPARSE) were optimized and verified by cable-loop experiments and tested with 1-mA TES experiments for two common electrode montages.Results: Comparisons between the volumetric methods against the 2D-MRCDI showed that relatively long acquisition times of 3D-DENSE using a single slab with six slices hindered the expected sensitivity improvement in the current-induced field measurements but improved sensitivity by 61% in the Laplacian of the field, on which some MRCDI reconstruction methods rely. Also, SMS-SPARSE acquisition of three slices, with a factor 2 CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) acceleration, performed best against the 2D-MRCDI with sensitivity improvements for the ΔBz,c and Laplacian noise floors of 56% and 78% (baseline without current flow) as well as 43% and 55% (current injection into head). SMS-SPARSE reached a sensitivity of 67 pT for three distant slices at 2 × 2 × 3 mm3 resolution in 10 min of total scan time, and consistently improved image quality.Conclusion: Volumetric MRCDI measurements with high sensitivity and image quality are well suited to characterize the TES field distribution in the human brain.

Purpose: An end-to-end differentiable 2D Bloch simulation is used to reduce T2 induced blurring in single-shot turbo spin echo sequences, also called rapid imaging with refocused echoes (RARE) sequences, by using a joint optimization of refocusing flip angles and a convolutional neural network.Methods: Simulation and optimization were performed in the MR-zero framework. Variable flip angle train and DenseNet parameters were optimized jointly using the instantaneous transverse magnetization, available in our simulation, at a certain echo time, which serves as ideal blurring-free target. Final optimized sequences were exported for in vivo measurements at a real system (3 T Siemens, PRISMA) using the Pulseq standard.Results: The optimized RARE was able to successfully lower T2 -induced blurring for single-shot RARE sequences in proton density-weighted and T2 -weighted images. In addition to an increased sharpness, the neural network allowed correction of the contrast changes to match the theoretical transversal magnetization. The optimization found flip angle design strategies similar to existing literature, however, visual inspection of the images and evaluation of the respective point spread function demonstrated an improved performance.Conclusions: This work demonstrates that when variable flip angles and a convolutional neural network are optimized jointly in an end-to-end approach, sequences with more efficient minimization of T2 -induced blurring can be found. This allows faster single- or multi-shot RARE MRI with longer echo trains.

Purpose: To extend the concept of 3D dynamic parallel imaging, we developed a prototype of an electronically reconfigurable dipole array that provides sensitivity alteration along the dipole length.Methods: We developed a radiofrequency array coil consisting of eight reconfigurable elevated-end dipole antennas. The receive sensitivity profile of each dipole can be electronically shifted toward one or the other end by electrical shortening or lengthening the dipole arms using positive-intrinsic-negative-diode lump-element switching units. Based on the results of electromagnetic simulations, we built the prototype and tested it at 9.4 T on phantom and healthy volunteer. A modified 3D SENSE reconstruction was used, and geometry factor (g-factor) calculations were performed to assess the new array coil.Results: Electromagnetic simulations showed that the new array coil was capable of alteration of its receive sensitivity profile along the dipole length. Electromagnetic and g-factor simulations showed closely agreeing predictions when compared to the measurements. The new dynamically reconfigurable dipole array provided significant improvement in geometry factor compared to static dipoles. We obtained up to 220% improvement for 3 × 2 (Ry × Rz ) acceleration compared to the static configuration case in terms of maximum g-factor and up to 54% in terms of mean g-factor for the same acceleration.Conclusion: We presented an 8-element prototype of a novel electronically reconfigurable dipole receive array that permits rapid sensitivity modulations along the dipole axes. Applying dynamic sensitivity modulation during image acquisition emulates two virtual rows of receive elements along the z-direction, and therefore improves parallel imaging performance for 3D acquisitions.

Automatic image segmentation at magnetic resonance imaging (MRI) of the brain is essential for a number of applications. Many well-known segmentation tools exist for the clinical domain. However, we have found that they become unreliable when applied to ultra-high resolution images and, in particular, to data acquired at magnetic field strength of 9.4 T. This has motivated us to develop a segmentation method that can handle images at ultra-high resolution ≤ 0.6 mm and field strengths 1.5–9.4 T. Specifically, we propose an adversarial game for flexible domain adaptation of convolutional neural networks in the context of brain MRI segmentation. In particular, we develop FLEXseg, the first brain MRI segmentation method suitable for images acquired at 9.4 T with 0.6 mm isotropic resolution. We demonstrate the performance of FLEXseg by comparing it with manually corrected labels approved by expert neuroradiologists.

Segmenting ultra high-field MR images is an important first step in many applications. Segmentation methods based on machine learning have been shown to be valuable tools for this purpose. However, for ultra high-field MR images ( 7 Tesla), a lack of training data is a problem. Therefore, in this work, we propose to use super-resolution for augmenting the training set. Specifically, we describe an efficient super-resolution model based on Generative Adversarial Network(GAN). It produces synthetic images that simulate MR data at ultra high isotropic resolutions of mm. We present the first results that show an improvement in segmentation accuracy of imaging data acquired at a 9.4 Tesla MRI scanner.

A method of processing magnetic resonance data of a sample under investigation includes the steps ofprovision of the MR data being collected with an MRI scanner apparatus, and machine learning based data analysis of the MR data by supplying the MR data to an artificial neural network being trained with predetermined training data, wherein at least one image parameter of the sample and additionally at least one uncertainty quantification measure representing a pre diction error ofthe at least one image parameter are provided by output elements of the neural network. Furthermore, a magnetic resonance imaging (MRI) scanner apparatus being adapted for employing the method ofprocessing MR data is described.

We propose a method to reduce blurring in RARE sequences along phase en-coding direction due to T2 decay during k-space acquisition. A convolutional neural net-work is employed to map simulated RARE im-ages to the transversal magnetization of the central echo.

Formation of healthy mammalian eggs from oocytes requires specialised F-actin structures. F-actin disruption produces aneuploid eggs, which are a leading cause of human embryo deaths, genetic disorders, and infertility. We found that oocytes regulate F-actin organisation and function by promptly transferring excess monomeric G-actin from the cytoplasm to the nucleus. Inside healthy oocyte nuclei, transferred monomers form dynamic F-actin structures, a conserved feature that significantly declines with maternal age. Monomer transfer must be controlled tightly. Blocked nuclear import of G-actin triggers assembly of a dense cytoplasmic F-actin network, while excess G-actin in the nucleus dramatically stabilises nuclear F-actin. Imbalances in either direction predispose oocytes to aneuploidy. The large oocyte nucleus is thus a homeostatic G-actin buffer that is used to maintain cytoplasmic F-actin form and function.One Sentence SummaryMammalian oocyte nuclei buffer cytosolic G-actin