Abstract: A method for magnetic resonance imaging includes generating a sampling pattern; acquiring k-space data during an MRI scan using the generated sampling pattern; and reconstructing an MRI image from the acquired k-space data. The sampling pattern is generated by storing a variable-density model of k-space based on Voronoi-cell statistics extracted from a set of optimized sampling patterns; receiving one or more acquisition parameters for a target MRI scan, the acquisition parameters including at least a target acceleration rate; calculating a target sampling density across k-space by applying the variable-density model to the one or more acquisition parameters; and generating a sampling pattern in real-time based on the target sampling density such that the target acceleration rate is achieved.

Abstract: A method for magnetic resonance imaging acquires time-resolved k-space data by a magnetic resonance imaging apparatus, and generates contrast-weighted images by a multi-task generator G from the time-resolved k-space data. The multi-task generator G comprises a deep learning neural network trained using prospectively under-sampled ground truth images acquired using an acceleration factor of at least 8 without any fully-sampled ground truth images. The multi-task generator G is also trained using a physics guidance model and a semi-supervised loss function.

Abstract: A method for magnetic resonance imaging (MRI) includes acquiring under-sampled k-space measurements from an MRI apparatus using multiple receiver coils; reconstructing an MRI image from the under-sampled k-space measurements and coil sensitivity maps using an unrolled neural network; generating reconstructed multi-coil k-space data from the MRI image by multiplying the MRI image by the coil sensitivity maps followed by performing a Fourier transform; estimating a k-space null-space convolutional kernel from fully-sampled k-space measurements in autocalibration signal lines of the under-sampled k-space measurements; solving a convex optimization problem to produce refined k-space data from the k-space null-space kernel, the under-sampled k-space measurements, and the reconstructed multi-coil k-space data; and producing a refined MRI image from the refined k-space data by performing an inverse Fourier transform followed by a coil combination using the coil sensitivity maps.

Abstract: A method for magnetic resonance imaging acquires time-resolved k-space data by a magnetic resonance imaging apparatus, and generates contrast-weighted images by a multi-task generator G from the time-resolved k-space data. The multi-task generator G comprises a deep learning neural network trained using prospectively under-sampled ground truth images acquired using an acceleration factor of at least 8 without any fully-sampled ground truth images. The multi-task generator G is also trained using a physics guidance model and a semi-supervised loss function.

Abstract: A method for magnetic resonance imaging (MRI) includes acquiring under-sampled k-space measurements from an MRI apparatus using multiple receiver coils; reconstructing an MRI image from the under-sampled k-space measurements and coil sensitivity maps using an unrolled neural network; generating reconstructed multi-coil k-space data from the MRI image by multiplying the MRI image by the coil sensitivity maps followed by performing a Fourier transform; estimating a k-space null-space convolutional kernel from fully-sampled k-space measurements in autocalibration signal lines of the under-sampled k-space measurements; solving a convex optimization problem to produce refined k-space data from the k-space null-space kernel, the under-sampled k-space measurements, and the reconstructed multi-coil k-space data; and producing a refined MRI image from the refined k-space data by performing an inverse Fourier transform followed by a coil combination using the coil sensitivity maps.