公开(公告)号：US20200337591A1

公开(公告)日：2020-10-29

申请号：US16394774

申请日：2019-04-25

Applicant: General Electric Company

Inventor： Michael Rotman ,  Rafael Shmuel Brada ,  Sangtae Ahn ,  Christopher Judson Hardy ,  Itzik Malkiel ,  Ron Wein

IPC: A61B5/055 ,  G01R33/565 ,  G01R33/48

Abstract: K-space data obtained from a magnetic resonance imaging scan where motion was detected is split into two parts in accordance with the timing of the motion to produce first and second sets of k-space data corresponding to different poses. Sub-images are reconstructed from the k first and second sets of k-space data, which are used as inputs to a deep neural network which transforms them into a motion-corrected image.

公开(公告)号：US11696700B2

公开(公告)日：2023-07-11

申请号：US16394774

申请日：2019-04-25

Applicant: General Electric Company

Inventor： Michael Rotman ,  Rafael Shmuel Brada ,  Sangtae Ahn ,  Christopher Judson Hardy ,  Itzik Malkiel ,  Ron Wein

IPC: A61B5/055 ,  G01R33/565 ,  G01R33/48 ,  G01R33/56 ,  G01R33/567

CPC classification number: A61B5/055 ,  G01R33/4818 ,  G01R33/56509 ,  G01R33/5608 ,  G01R33/5676

Abstract: K-space data obtained from a magnetic resonance imaging scan where motion was detected is split into two parts in accordance with the timing of the motion to produce first and second sets of k-space data corresponding to different poses. Sub-images are reconstructed from the k first and second sets of k-space data, which are used as inputs to a deep neural network which transforms them into a motion-corrected image.

公开(公告)号：US10996306B2

公开(公告)日：2021-05-04

申请号：US16394791

申请日：2019-04-25

Applicant: General Electric Company

Inventor： Isabelle Heukensfeldt Jansen ,  Sangtae Ahn ,  Christopher Judson Hardy ,  Itzik Malkiel ,  Rafael Shmuel Brada ,  Ron Wein ,  Michael Rotman

IPC: G06T7/20 ,  G01R33/565 ,  G01R33/56 ,  G01R33/48 ,  G06T11/00 ,  G06T7/00 ,  G06K9/62 ,  G06N3/08

Abstract: A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.

公开(公告)号：US10991101B2

公开(公告)日：2021-04-27

申请号：US16299743

申请日：2019-03-12

Applicant: General Electric Company

Inventor： Rafi Shmuel Brada ,  Ron Wein ,  Gregory Wilson ,  Alberto Santamaria-Pang ,  Leonid Gugel

IPC: G06T7/10

Abstract: The example embodiments are directed to refinement process for generating an accurate image segmentation map. A refinement network may enhance an initially generated segmentation map using a model that is trained using synthetic images. In one example, the method may include storing an image of content which includes a plurality of categories of data, receiving an initial segmentation map of the image, the initial segmentation map comprising pixel probability values with respect to the plurality of categories, executing a refinement predictive model on the initial segmentation map and the image to generate a refined segmentation map, wherein the predictive model is trained using synthetic images of the plurality of categories of data, and generating a segmented image based on the refined segmentation map.

公开(公告)号：US20200341100A1

公开(公告)日：2020-10-29

申请号：US16394791

申请日：2019-04-25

Applicant: General Electric Company

Inventor： Isabelle Heukensfeldt Jansen ,  Sangtae Ahn ,  Christopher Judson Hardy ,  Itzik Malkiel ,  Rafael Shmuel Brada ,  Ron Wein ,  Michael Rotman

IPC: G01R33/565 ,  G01R33/56 ,  G01R33/48 ,  G06T11/00 ,  G06T7/00 ,  G06T7/20 ,  G06K9/62 ,  G06N3/08

Abstract: A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.