Ge Precision Healthcare LLC

Arnaud Guidon

Robert Marc Lebel

Ersin Bayram

Daniel Vance Litwiller

Xinzeng Wang

Tim Sprenger

Gaohong G Wu

Suchandrima Banerjee

Jaemin Shin

Baolian Yang

Douglas Link

Daniel Vanc Litwiller

A computer-implemented method and system for performing magnetic resonance diffusion weighted imaging of an object includes generating, via a processor, a diffusion weighted imaging sequence where all diffusion gradients are allocated into N groups such that all imaging slices are acquired for all the diffusion gradients corresponding to a group of the N groups back-to-back in A*TR, wherein N is two or larger, wherein Ais a number of diffusion gradients of each group of the N groups and TR is repetition time, and wherein in each TR every diffusion gradient of the group of the N groups is applied at least once. The computer-implemented method and system also includes acquiring, via the processor, imaging slices for multiple groups of the N groups utilizing the diffusion weighted imaging sequence during a scan of the object utilizing a magnetic resonance imaging scanner.

System and method for diffusion group cycling

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.

System and method for reconstruction using a high-resolution phase in magnetic resonance images

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.

Deep learning systems and methods of removal of truncation artifacts in magnetic resonance images

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.

Systems and methods of generating robust phase images in magnetic resonance images

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Somerville, MA, United States of America

Arnaud Guidon