公开(公告)号：US20220237748A1

公开(公告)日：2022-07-28

申请号：US17718697

申请日：2022-04-12

Applicant: GE Precision Healthcare LLC

Inventor： Xinzeng Wang ,  Daniel Vance Litwiller ,  Sagar Mandava ,  Robert Marc Lebel ,  Graeme Colin Mckinnon ,  Ersin Bayram

IPC: G06T5/00 ,  G06T7/00 ,  G06N3/08 ,  G16H30/40

Abstract: Methods and systems are provided for independently removing streak artifacts and noise from medical images, using trained deep neural networks. In one embodiment, streak artifacts and noise may be selectively and independently removed from a medical image by receiving the medical image comprising streak artifacts and noise, mapping the medical image to a streak residual and a noise residual using the trained deep neural network, subtracting the streak residual from the medical image to a first extent, and subtracting the noise residual from the medical image to a second extent, to produce a de-noised medical image, and displaying the de-noised medical image via a display device.

公开(公告)号：US12045917B2

公开(公告)日：2024-07-23

申请号：US17131171

申请日：2020-12-22

Applicant: GE PRECISION HEALTHCARE LLC

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

IPC: G06T11/00 ,  A61B5/00 ,  A61B5/055 ,  G01R33/56 ,  G01R33/565 ,  G06T7/00

CPC classification number: G06T11/008 ,  A61B5/055 ,  A61B5/7203 ,  A61B5/7267 ,  G01R33/5608 ,  G01R33/565 ,  G06T7/0012 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30004

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.

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：US20210272240A1

公开(公告)日：2021-09-02

申请号：US16806689

申请日：2020-03-02

Applicant: GE Precision Healthcare LLC

Inventor： Daniel Litwiller ,  Xinzeng Wang ,  Ali Ersoz ,  Robert Marc Lebel ,  Ersin Bayram ,  Graeme Colin McKinnon

IPC: G06T5/00 ,  G06T7/00 ,  A61B6/00

Abstract: Methods and systems are provided for de-noising medical images using deep neural networks. In one embodiment, a method comprises receiving a medical image acquired by an imaging system, wherein the medical image comprises colored noise; mapping the medical image to a de-noised medical image using a trained convolutional neural network (CNN); and displaying the de-noised medical image via a display device. The deep neural network may thereby reduce colored noise in the acquired noisy medical image, increasing a clarity and diagnostic quality of the image.