Abstract: A method for high spatial and temporal resolution dynamic contrast enhanced magnetic resonance imaging using a random subsampled Cartesian k-space using a Poisson-disk random pattern acquisition strategy and a compressed sensing reconstruction algorithm incorporating magnitude image subtraction is presented. One reconstruction uses a split-Bregman minimization of the sum of the L1 norm of the pixel-wise magnitude difference between two successive temporal frames, a fidelity term and a total variation (TV) sparsity term.

Abstract: In a method and magnetic resonance (MR) apparatus for MR image reconstruction, an image reconstruction algorithm is used that operates on one calibration matrix that is formed by reorganizing a number of individual, undersampled k-space data sets respectively acquired by multiple reception coils in a parallel MR data acquisition from a subject exhibiting motion. The motion causes the k-space data sets to exhibit errors. In order to use the calibration matrix in the reconstruction algorithm, it is subjected to an iterative rank reduction procedure in which, in each iteration, a residual is calculated for each data point that represents how poorly, due to motion-induced corruptions, that data point satisfies the low rank constraint, and non-satisfying data points are removed from the data point for the next iteration. The resulting low rank matrix at the end of the iterations is then used to produce images with fewer motion-induced errors.

Abstract: In a method and magnetic resonance (MR) apparatus for MR image reconstruction, an image reconstruction algorithm is used that operates on one calibration matrix that is formed by reorganizing a number of individual, undersampled k-space data sets respectively acquired by multiple reception coils in a parallel MR data acquisition from a subject exhibiting motion. The motion causes the k-space data sets to exhibit errors. In order to use the calibration matrix in the reconstruction algorithm, it is subjected to an iterative rank reduction procedure in which, in each iteration, a residual is calculated for each data point that represents how poorly, due to motion-induced corruptions, that data point satisfies the low rank constraint, and non-satisfying data points are removed from the data point for the next iteration. The resulting low rank matrix at the end of the iterations is then used to produce images with fewer motion-induced errors.

Abstract: A method for high spatial and temporal resolution dynamic contrast enhanced magnetic resonance imaging using a random subsampled Cartesian k-space using a Poisson-disk random pattern acquisition strategy and a compressed sensing reconstruction algorithm incorporating magnitude image subtraction is presented. One reconstruction uses a split-Bregman minimization of the sum of the L1 norm of the pixel-wise magnitude difference between two successive temporal frames, a fidelity term and a total variation (TV) sparsity term.

Abstract: A method for high spatial and temporal resolution dynamic contrast enhanced magnetic resonance imaging using a random subsampled Cartesian k-space using a Poisson-disk random pattern acquisition strategy and a compressed sensing reconstruction algorithm incorporating magnitude image subtraction is presented. One reconstruction uses a split-Bregman minimization of the sum of the L1 norm of the pixel-wise magnitude difference between two successive temporal frames, a fidelity term and a total variation (TV) sparsity term.

Abstract: Self-gating methods and Systems are provided for cardiac imaging analysis. In particular, non-phased coded self-gating data are collected separately from imaging data. The method uses multiple coil arrays to repeatedly acquire self-gating signals that are separate from image acquisitions. Learning-based algorithms are used in data processing to detect a triggering event, such as the onset of a heartbeat.

Abstract: A method for high spatial and temporal resolution dynamic contrast enhanced magnetic resonance imaging using a random subsampled Cartesian k-space using a Poisson-disk random pattern acquisition strategy and a compressed sensing reconstruction algorithm incorporating magnitude image subtraction is presented. One reconstruction uses a split-Bregman minimization of the sum of the L1 norm of the pixel-wise magnitude difference between two successive temporal frames, a fidelity term and a total variation (TV) sparsity term.