Abstract: A method for producing an image of a subject with a magnetic resonance imaging (MRI) comprises acquiring a first set of partial k-space data from the subject and generating a phase corrected image based on a phase correction factor and the first set of the partial k-space data. The method further includes transforming the phase corrected image into a second set of partial k-space data and reconstructing the image of the subject from the second set of the partial k-space data and a weighting function.

Abstract: A computer-implemented method of removing truncation artifacts in magnetic resonance (MR) images is provided. The method includes receiving a crude image that is based on partial k-space data from a partial k-space that is asymmetrically truncated in at least one k-space dimension. The method also includes analyzing the crude image using a neural network model trained with a pair of pristine images and corrupted images. The corrupted images are based on partial k-space data from partial k-spaces truncated in one or more partial sampling patterns. The pristine images are based on full k-space data corresponding to the partial k-space data of the corrupted images, and target output images of the neural network model are the pristine images. The method further includes deriving an improved image of the crude image based on the analysis, wherein the derived improved image includes reduced truncation artifacts and increased high spatial frequency data.

Abstract: A computer-implemented method of correcting phase and reducing noise in magnetic resonance (MR) phase images is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced, and target output images of the neural network model are the pristine images. The method further includes receiving MR images including corrupted phase information, and analyzing the received MR images using the neural network model. The method also includes deriving pristine phase images of the received MR images based on the analysis, wherein the derived pristine phase images include reduced corrupted phase information, compared to the received MR images, and outputting MR images based on the derived pristine phase images.

Abstract: A computer-implemented method of correcting phase and reducing noise in magnetic resonance (MR) phase images is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced, and target output images of the neural network model are the pristine images. The method further includes receiving MR images including corrupted phase information, and analyzing the received MR images using the neural network model. The method also includes deriving pristine phase images of the received MR images based on the analysis, wherein the derived pristine phase images include reduced corrupted phase information, compared to the received MR images, and outputting MR images based on the derived pristine phase images.

Abstract: A method for magnetic resonance imaging includes unwrapping a calibration image based on coil sensitivity data obtained according to an array spatial sensitivity encoding technique and acquiring raw scan data of a plurality of MRI scan shots. The method further includes reconstructing an aliased image for each of the MRI scan shots, reconstructing an unaliased image for each of the MRI scan shots, according to the calibration image, recovering a plurality of pseudo-sensitivity maps from the plurality of unaliased images and from the calibration image, and unwrapping at least one final unaliased image from the plurality of aliased images, according to the plurality of pseudo-sensitivity maps.

Abstract: A method for magnetic resonance imaging includes unwrapping a calibration image based on coil sensitivity data obtained according to an array spatial sensitivity encoding technique and acquiring raw scan data of a plurality of MRI scan shots. The method further includes reconstructing an aliased image for each of the MRI scan shots, reconstructing an unaliased image for each of the MRI scan shots, according to the calibration image, recovering a plurality of pseudo-sensitivity maps from the plurality of unaliased images and from the calibration image, and unwrapping at least one final unaliased image from the plurality of aliased images, according to the plurality of pseudo-sensitivity maps.