Abstract: Image reconstruction systems and methods include providing sensitivity maps for coils of a magnetic resonance imaging (MRI) system to a neural network. The systems and methods also include providing interleaved k-space data to the neural network, wherein the interleaved k-space data includes partial k-space data interleaved with zeros, or synthesized k-space data, to provide an extended field of view (FOV) different from a FOV utilized during acquisition of the partial k-space data, wherein the partial k-space data were obtained during a scan of a region of interest with the MRI system. The systems and methods further include outputting, from the neural network, a final reconstructed MR image based at least on the sensitivity maps and the interleaved k-space data, wherein the final reconstructed MR image includes the FOV utilized during the acquisition of the partial k-space data.

Abstract: A magnetic resonance (MR) image processing system is provided. The system includes an MR image processing computing device that includes at least one processor. The processor is programmed to execute a neural network model configured to receive crude MR data as an input and output processed MR images associated with the crude MR data, the crude MR data and the processed MR images having the first number of dimensions. The processor is also programmed to receive a pair of pristine data and corrupted data both having a second number of dimensions lower than the first number of dimensions. The corrupted data are the pristine data added with primitive features. The processor is further programmed to train the neural network model using the pair of the pristine data and the corrupted data. The trained neural network model is configured to change primitive features associated with the crude MR data.

Abstract: A magnetic resonance (MR) imaging method of correcting motion in precorrection MR images of a subject is provided. The method includes applying, by an MR system, a pulse sequence having a k-space trajectory of a blade being rotated in k-space. The method also includes acquiring k-space data of a three-dimensional (3D) imaging volume of the subject, the k-space data of the 3D imaging volume corresponding to the precorrection MR images and acquired by the pulse sequence. The method further includes receiving a 3D MR calibration data of a 3D calibration volume, wherein the 3D calibration volume is greater than or equal to the 3D imaging volume, jointly estimating rotation and translation in the precorrection MR images based on the k-space data of the 3D imaging volume and the calibration data, correcting motion in the precorrection images based on the estimated rotation and the estimated translation, and outputting the motion-corrected images.

Abstract: Image reconstruction systems and methods include providing sensitivity maps for coils of a magnetic resonance imaging (MRI) system to a neural network. The systems and methods also include providing interleaved k-space data to the neural network, wherein the interleaved k-space data includes partial k-space data interleaved with zeros, or synthesized k-space data, to provide an extended field of view (FOV) different from a FOV utilized during acquisition of the partial k-space data, wherein the partial k-space data were obtained during a scan of a region of interest with the MRI system. The systems and methods further include outputting, from the neural network, a final reconstructed MR image based at least on the sensitivity maps and the interleaved k-space data, wherein the final reconstructed MR image includes the FOV utilized during the acquisition of the partial k-space data.