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1.发明授权公开(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
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.
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公开(公告)号：US12125175B2
公开(公告)日：2024-10-22
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
CPC classification number: G06T5/70 , G06N3/08 , G06T7/0012 , G06V10/82 , G16H30/40 , G06T2207/10081 , G06T2207/10088 , G06T2207/20084 , G06T2207/30004
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.
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3.发明申请公开(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
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.
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4.发明授权公开(公告)号：US11783451B2
公开(公告)日：2023-10-10
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
CPC classification number: G06T5/002 , A61B6/5258 , G06T7/0012 , G06T2207/10024 , G06T2207/10088 , G06T2207/20081 , G06T2207/20182
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.
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公开(公告)号：US20210295474A1
公开(公告)日：2021-09-23
申请号：US16827422
申请日：2020-03-23
Applicant: GE Precision Healthcare LLC
Inventor： Xinzeng Wang , Daniel Vance Litwiller , Sagar Mandava , Robert Marc Lebel , Graeme Colin McKinnon , Ersin Bayram
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.
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Patent Agency Ranking
6.发明申请公开(公告)号：US20220198725A1
公开(公告)日：2022-06-23
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
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.
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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.
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公开(公告)号：US11341616B2
公开(公告)日：2022-05-24
申请号：US16827422
申请日：2020-03-23
Applicant: GE Precision Healthcare LLC
Inventor： Xinzeng Wang , Daniel Vance Litwiller , Sagar Mandava , Robert Marc Lebel , Graeme Colin McKinnon , Ersin Bayram
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.
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公开(公告)号：US20250131614A1
公开(公告)日：2025-04-24
申请号：US18493698
申请日：2023-10-24
Applicant: GE Precision Healthcare LLC
Inventor： Xinzeng Wang , Xucheng Zhu , Graeme C. Mckinnon
Abstract: The current disclosure provides methods and systems to reduce an amount of artifact in image data. In one example, a method for an image processing system comprises generating a plurality of simulated images, generating a set of training image pairs based on the plurality of simulated magnetic resonance images, training an artifact removal neural network using the set of training image pairs, and generating an output of the artifact removal neural network based on an inputted acquired image, wherein generating the plurality of simulated images comprises generating images from RGB images, simulating motion in the simulated images, simulating contrast in the simulated images, and simulating phase contrast dynamics in the simulated images.
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公开(公告)号：US20240410966A1
公开(公告)日：2024-12-12
申请号：US18330647
申请日：2023-06-07
Applicant: GE Precision Healthcare LLC
Inventor： Xinzeng Wang , Sagar Mandava , Xucheng Zhu
Abstract: A computer-implemented method of reducing artifacts in multi-channel magnetic resonance (MR) images is provided. The method includes receiving a plurality of sets of MR images acquired by a radio-frequency (RF) coil assembly having a plurality of channels. Each set of MR images includes a plurality of slices of MR images acquired by one of the plurality of channels. The method also includes estimating a plurality of sets of artifacts in the plurality of sets of MR images by inputting the plurality of sets of MR images into a neural network model. Each set of artifacts corresponds to the one of the plurality of channels. The method further includes reducing artifacts in the plurality of sets of MR images based on estimated artifacts, deriving MR images of reduced artifacts by combining the MR images of reduced artifacts, and outputting the MR images of reduced artifacts.
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