Abstract: Systems and methods include conversion of a first frame of k-space data acquired using a first initial readout polarity to first hybrid (kx, y)-space data, conversion of a second frame of k-space data acquired using a second initial readout polarity to second hybrid (kx, y)-space data, determination of a relationship between phase difference and y-position based on phase differences between a plurality of pixels located at kx=a of first hybrid (kx, y)-space data and a plurality of pixels at kx=b of second hybrid (kx, y)-space data, where a and b are constants, modification of the second hybrid (kx, y)-space data based on the relationship, conversion of the modified second hybrid (kx, y)-space data to a modified second frame of k-space data, generation of two single-polarity readout k-space frames based on the first frame of k-space data and the modified second frame of k-space data, and correction of a third frame of EPI image data based on the two single-readout polarity k-space frames.

Abstract: Systems and methods include conversion of a first frame of k-space data acquired using a first initial readout polarity to first hybrid (kx, y)-space data, conversion of a second frame of k-space data acquired using a second initial readout polarity to second hybrid (kx, y)-space data, determination of a relationship between phase difference and y-position based on phase differences between a plurality of pixels located at kx=a of first hybrid (kx, y)-space data and a plurality of pixels at kx=b of second hybrid (kx, y)-space data, where a and b are constants, modification of the second hybrid (kx, y)-space data based on the relationship, conversion of the modified second hybrid (kx, y)-space data to a modified second frame of k-space data, generation of two single-polarity readout k-space frames based on the first frame of k-space data and the modified second frame of k-space data, and correction of a third frame of EPI image data based on the two single-readout polarity k-space frames.

Abstract: Systems and methods for combined ghost artifact correction and parallel imaging reconstruction of simultaneous multislice (“SMS”) magnetic resonance imaging (“MRI”) data are provided. Dual-polarity training data are used to generate ghost-free slice data, which are used as target data in a reconstruction kernel training process. The training data are used as source data in the reconstruction kernel training. As a result, reconstruction kernels are computed, which can be used to reconstruct images from SMS data in which slice-specific ghosting artifacts are removed.

Abstract: Systems and methods for combined ghost artifact correction and parallel imaging reconstruction of simultaneous multislice (“SMS”) magnetic resonance imaging (“MRI”) data are provided. Dual-polarity training data are used to generate ghost-free slice data, which are used as target data in a reconstruction kernel training process. The training data are used as source data in the reconstruction kernel training. As a result, reconstruction kernels are computed, which can be used to reconstruct images from SMS data in which slice-specific ghosting artifacts are removed.

Abstract: Reconstructing an image from MRI data provided by an MRI machine includes: selecting a system transformation based on the MRI machine; selecting a subspace based on the system transformation; and obtaining a solution vector that solves a minimization problem. The minimization problem is formulated on the subspace, based on the MRI data. The image is reconstructed from the solution vector and displayed.

Abstract: Reconstructing an image from MRI data provided by an MRI machine includes: selecting a system transformation based on the MRI machine; selecting a subspace based on the system transformation; and obtaining a solution vector that solves a minimization problem. The minimization problem is formulated on the subspace, based on the MRI data. The image is reconstructed from the solution vector and displayed.