公开(公告)号：US11346912B2

公开(公告)日：2022-05-31

申请号：US16937324

申请日：2020-07-23

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Arnaud Guidon ,  Xinzeng Wang ,  Daniel Vance Litwiller ,  Tim Sprenger ,  Robert Marc Lebel ,  Ersin Bayram

IPC: G01R33/565 ,  G01R33/56 ,  G01R33/48 ,  G01R33/563

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.

公开(公告)号：US20220026516A1

公开(公告)日：2022-01-27

申请号：US16937324

申请日：2020-07-23

Applicant: GE PRECISION HEALTHCARE LLC

Inventor： Arnaud Guidon ,  Xinzeng Wang ,  Daniel Vance Litwiller ,  Tim Sprenger ,  Robert Marc Lebel ,  Ersin Bayram

IPC: G01R33/565 ,  G01R33/56 ,  G01R33/48 ,  G01R33/563

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.

公开(公告)号：US12201412B2

公开(公告)日：2025-01-21

申请号：US17172644

申请日：2021-02-10

Applicant: GE Precision Healthcare LLC

Inventor： Xinzeng Wang ,  Daniel V. Litwiller ,  Arnaud Guidon ,  Ersin Bayram ,  Robert Marc Lebel ,  Tim Sprenger

IPC: A61B5/055 ,  A61B5/00 ,  G01R33/46 ,  G01R33/48 ,  G01R33/56

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.

公开(公告)号：US20220248972A1

公开(公告)日：2022-08-11

申请号：US17172644

申请日：2021-02-10

Applicant: GE Precision Healthcare LLC

Inventor： Xinzeng Wang ,  Daniel V. Litwiller ,  Arnaud Guidon ,  Ersin Bayram ,  Robert Marc Lebel ,  Tim Sprenger

IPC: A61B5/055 ,  A61B5/00 ,  G01R33/56 ,  G01R33/46 ,  G01R33/48

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.