公开(公告)号：US11042803B2

公开(公告)日：2021-06-22

申请号：US16276135

申请日：2019-02-14

Applicant: General Electric Company

Inventor： Itzik Malkiel ,  Christopher Judson Hardy

IPC: G06K9/00 ,  G06N3/08 ,  G06N3/04 ,  G06T3/40 ,  G06N20/20

Abstract: A method of reconstructing imaging data into a reconstructed image may include training a generative adversarial network (GAN) to reconstruct the imaging data. The GAN may include a generator and a discriminator. Training the GAN may include determining a combined loss by adaptively adjusting an adversarial loss based at least in part on a difference between the adversarial loss and a pixel-wise loss. Additionally, the combined loss may be a combination of the adversarial loss and the pixel-wise loss. Training the GAN may also include updating the generator based at least in part on the combined loss. The method may also include receiving, into the generator, the imaging data and reconstructing, via the generator, the imaging data into a reconstructed image.

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：US20190266761A1

公开(公告)日：2019-08-29

申请号：US15907797

申请日：2018-02-28

Applicant: GENERAL ELECTRIC COMPANY

Inventor： Itzik Malkiel ,  Sangtae Ahn ,  Christopher Judson Hardy

IPC: G06T11/00 ,  G01R33/561 ,  G01R33/48

Abstract: A method for sparse image reconstruction includes acquiring coil data from a magnetic resonance imaging device. The coil data includes undersampled k-space data corresponding to a subject. The method further includes processing the coil data using an image reconstruction technique to generate an initial undersampled image. The method also includes generating a reconstructed image based on the coil data, the initial undersampled image, and a plurality of iterative blocks of a flared network. A first iterative block of the flared network receives the initial undersampled image. Each of the plurality of iterative blocks includes a data consistency unit and a regularization unit and the iterative blocks are connected both by direct connections from one iterative block to the following iterative block and by a plurality of dense skip connections to non-adjacent iterative blocks. The flared network is based on a neural network trained using previously acquired coil data.

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：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.

公开(公告)号：US20200265318A1

公开(公告)日：2020-08-20

申请号：US16276135

申请日：2019-02-14

Applicant: General Electric Company

Inventor： Itzik Malkiel ,  Christopher Judson Hardy

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06T3/40

Abstract: A method of reconstructing imaging data into a reconstructed image may include training a generative adversarial network (GAN) to reconstruct the imaging data. The GAN may include a generator and a discriminator. Training the GAN may include determining a combined loss by adaptively adjusting an adversarial loss based at least in part on a difference between the adversarial loss and a pixel-wise loss. Additionally, the combined loss may be a combination of the adversarial loss and the pixel-wise loss. Training the GAN may also include updating the generator based at least in part on the combined loss. The method may also include receiving, into the generator, the imaging data and reconstructing, via the generator, the imaging data into a reconstructed image.

公开(公告)号：US11175365B2

公开(公告)日：2021-11-16

申请号：US16150079

申请日：2018-10-02

Applicant: General Electric Company

Inventor： Christopher Judson Hardy ,  Itzik Malkiel

IPC: G01R33/561 ,  G01R33/56 ,  G06T7/00

Abstract: A method is provided that includes acquiring coil data from a magnetic resonance imaging device. The coil data includes undersampled k-space data. The method includes processing the coil data using an image reconstruction technique to generate an initial undersampled image. The method includes generating a reconstructed image based on the coil data, the initial undersampled image, and multiple iterative blocks of a residual deep-learning image reconstruction network. A first iterative block of the residual deep-learning image reconstruction network receives the initial undersampled image. Each of the multiple iterative blocks includes a data-consistency unit that preserves the fidelity of the coil data in a respective output of a respective iterative block utilizing zeroed data consistency. The initial undersampled image is added to an output of the last iterative block via a residual connection. The residual deep-learning image reconstruction network is a neural network trained using previously acquired coil data.