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Purpose: A multi-coil shim setup is designed and optimized for human brain shimming. Here, the size and position of a set of square coils are optimized to improve the shim performance without increasing the number of local coils. Utilizing such a setup is especially beneficial at ultrahigh fields where B 0 inhomogeneity in the human brain is more severe., Methods: The optimization started with a symmetric arrangement of 32 independent coils. Three parameters per coil were optimized in parallel, including angular and axial positions on a cylinder surface and size of the coil, which were constrained by cylinder size, construction consideration, and amplifiers specifications. B 0 maps were acquired at 9.4T in 8 healthy volunteers for use as training data. The global and dynamic shimming performance of the optimized multi-coil were compared in simulations and measurements to a symmetric design and to the scanner's second-order shim setup, respectively., Results: The optimized multi-coil performs better by 14.7% based on standard deviation (SD) improvement with constrained global shimming in comparison to the symmetric positioning of the coils. Global shimming performance was comparable with a symmetric 65-channel multi-coil and full fifth-order spherical harmonic shim coils. On average, an SD of 48.4 and 31.9 Hz was achieved for in vivo measurements after global and dynamic slice-wise shimming, respectively., Conclusions: An optimized multi-coil shim setup was designed and constructed for human whole-brain shimming. Similar performance of the multi-coils with many channels can be achieved with a fewer number of channels when the coils are optimally arranged around the target., (© 2019 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: A novel method for the acceleration of MRI acquisition is proposed that relies on the local modulation of magnetic fields. These local modulations provide additional spatial information for image reconstruction that is used to accelerate image acquisition., Methods: In experiments and simulations, eight local coils connected to current amplifiers were used for rapid local magnetic field variation. Acquired and simulated data were reconstructed to quantify reconstruction errors as a function of the acceleration factor and applied modulation frequency and strength., Results: Experimental results demonstrate a possible acceleration factor of 2 to 4. Simulations demonstrate the challenges and limits of this method in terms of required magnetic field modulation strengths and frequencies. A normalized mean squared error of below 10% can be achieved for acceleration factors of up to 8 using modulation field strengths comparable to the readout gradient strength at modulation frequencies in the range of 5 to 20 kHz., Conclusion: Spread-spectrum MRI represents a new approach to accelerate image acquisition, and it can be independently combined with traditional parallel imaging techniques based on local receive coil sensitivities., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: Physiological nonrigid motion is inevitable when imaging, e.g., abdominal viscera, and can lead to serious deterioration of the image quality. Prospective techniques for motion correction can handle only special types of nonrigid motion, as they only allow global correction. Retrospective methods developed so far need guidance from navigator sequences or external sensors. We propose a fully retrospective nonrigid motion correction scheme that only needs raw data as an input., Methods: Our method is based on a forward model that describes the effects of nonrigid motion by partitioning the image into patches with locally rigid motion. Using this forward model, we construct an objective function that we can optimize with respect to both unknown motion parameters per patch and the underlying sharp image., Results: We evaluate our method on both synthetic and real data in 2D and 3D. In vivo data was acquired using standard imaging sequences. The correction algorithm significantly improves the image quality. Our compute unified device architecture (CUDA)-enabled graphic processing unit implementation ensures feasible computation times., Conclusion: The presented technique is the first computationally feasible retrospective method that uses the raw data of standard imaging sequences, and allows to correct for nonrigid motion without guidance from external motion sensors., (© 2014 Wiley Periodicals, Inc.)

Purpose: Subject motion can severely degrade MR images. A retrospective motion correction algorithm, Gradient-based motion correction, which significantly reduces ghosting and blurring artifacts due to subject motion was proposed. The technique uses the raw data of standard imaging sequences; no sequence modifications or additional equipment such as tracking devices are required. Rigid motion is assumed., Methods: The approach iteratively searches for the motion trajectory yielding the sharpest image as measured by the entropy of spatial gradients. The vast space of motion parameters is efficiently explored by gradient-based optimization with a convergence guarantee., Results: The method has been evaluated on both synthetic and real data in two and three dimensions using standard imaging techniques. MR images are consistently improved over different kinds of motion trajectories. Using a graphics processing unit implementation, computation times are in the order of a few minutes for a full three-dimensional volume., Conclusion: The presented technique can be an alternative or a complement to prospective motion correction methods and is able to improve images with strong motion artifacts from standard imaging sequences without requiring additional data., (Copyright © 2013 Wiley Periodicals, Inc., a Wiley company.)

The optimization of k-space sampling for nonlinear sparse MRI reconstruction is phrased as a Bayesian experimental design problem. Bayesian inference is approximated by a novel relaxation to standard signal processing primitives, resulting in an efficient optimization algorithm for Cartesian and spiral trajectories. On clinical resolution brain image data from a Siemens 3T scanner, automatically optimized trajectories lead to significantly improved images, compared to standard low-pass, equispaced, or variable density randomized designs. Insights into the nonlinear design optimization problem for MRI are given., (Copyright (c) 2009 Wiley-Liss, Inc.)