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公开(公告)号：US20200160509A1
公开(公告)日：2020-05-21
申请号：US16192551
申请日：2018-11-15
Applicant: General Electric Company
Inventor： Jed Douglas Pack , Peter Michael Edic , Xin Wang , Xia Li , Prem Venugopal , James Vradenburg Miller
Abstract: The present disclosure relates to training one or more neural networks for vascular vessel assessment using synthetic image data for which ground-truth data is known. In certain implementations, the synthetic image data may be based in part, or derived from, clinical image data for which ground-truth data is not known or available. Neural networks trained in this manner may be used to perform one or more of vessel segmentation, decalcification, Hounsfield unit scoring, and/or estimation of a hemodynamic parameter.
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2.发明申请公开(公告)号：US20190026608A1
公开(公告)日：2019-01-24
申请号：US16126762
申请日：2018-09-10
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
CPC classification number: G06K9/6265 , G06K9/036 , G06K9/4604 , G06K9/4628 , G06N3/04 , G06N3/0454 , G06N3/08 , G06N3/084 , G06T7/0002 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/10116 , G06T2207/10132 , G06T2207/30168
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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公开(公告)号：US09943280B2
公开(公告)日：2018-04-17
申请号：US15062838
申请日：2016-03-07
Applicant: General Electric Company
Inventor： Xin Wang , Bernhard Erich Hermann Claus , Cynthia Elizabeth Landberg Davis , Razvan Gabriel Iordache
CPC classification number: A61B6/502 , A61B5/1075 , A61B6/025 , A61B6/0414 , A61B6/4417 , A61B6/461 , A61B6/5205 , A61B6/5217 , A61B6/5258 , A61B8/0825 , A61B2090/3908 , A61B2090/3937 , A61B2090/3966 , G06F19/00 , G06T11/003 , G06T2207/10112 , G06T2207/30004 , G06T2207/30068 , G16H50/50
Abstract: A method of breast image reconstruction includes positioning a breast on an imaging system support plate, compressing the breast with a flexible paddle, obtaining imaging data, estimating a breast thickness profile by at least one of placing markers on the breast, performing an image-based analysis of the obtained data, using an auxiliary system, and performing a model-based computation. The three dimensional reconstruction including using a thickness profile of the breast surface in at least one of an iterative reconstruction, a filtered back-projection reconstruction, and a joint reconstruction performed using information obtained from an ultrasound scan. A non-transitory medium having executable instructions to cause a processor to perform the method is also disclosed.
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4.发明授权公开(公告)号：US10896352B2
公开(公告)日：2021-01-19
申请号：US16697904
申请日：2019-11-27
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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公开(公告)号：US10881353B2
公开(公告)日：2021-01-05
申请号：US16430160
申请日：2019-06-03
Applicant: General Electric Company
Inventor： David Andrew Shoudy , John Eric Tkaczyk , Xin Wang , Heather Chan
Abstract: A method includes generating a three-dimensional (3D) surface map associated with a patient from a patient sensor, generating a 3D patient space from the 3D surface map associated with the patient, determining a current pose associated with the patient based on the 3D surface map associated with the patient, comparing the current pose with a desired pose associated with the patient with respect to an imaging system, determining a recommended movement based on the comparison between the current pose and the desired pose, and providing an indication of the recommended movement. The desired pose facilitates imaging of an anatomical feature of the patient by the imaging system and the recommended movement may reposition the patient in the desired pose.
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Patent Agency Ranking
6.发明授权公开(公告)号：US10565477B2
公开(公告)日：2020-02-18
申请号：US16511972
申请日：2019-07-15
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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公开(公告)号：US10456101B2
公开(公告)日：2019-10-29
申请号：US15935879
申请日：2018-03-26
Applicant: General Electric Company
Inventor： Xin Wang , Bernhard Erich Hermann Claus , Cynthia Elizabeth Landberg Davis , Razvan Gabriel Iordache
IPC: A61B6/04 , A61B6/00 , A61B8/08 , A61B6/02 , G06F19/00 , G16H50/50 , A61B90/00 , A61B5/107 , G06T11/00
Abstract: A method of breast image reconstruction includes positioning a breast on an imaging system support plate, compressing the breast with a flexible paddle, obtaining imaging data, estimating a breast thickness profile by at least one of placing markers on the breast, performing an image-based analysis of the obtained data, using an auxiliary system, and performing a model-based computation. The three-dimensional (3D) reconstruction including using a thickness profile of the breast surface in at least one of an iterative reconstruction, a filtered back-projection reconstruction, and a joint reconstruction performed using information obtained from an ultrasound scan. A non-transitory medium having executable instructions to cause a processor to perform the method is also disclosed.
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8.发明申请公开(公告)号：US20180144214A1
公开(公告)日：2018-05-24
申请号：US15360742
申请日：2016-11-23
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
CPC classification number: G06K9/6265 , G06K9/4604 , G06N3/0454 , G06N3/084 , G06T7/0012 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/10108 , G06T2207/10116 , G06T2207/10132 , G06T2207/30168
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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9.发明申请METHOD FOR MEASURING LIVER FAT MASS USING DUAL-ENERGY X-RAY ABSORPTIOMETRY 有权使用双能量X射线吸收测定法测量肝脏质量的方法公开(公告)号：US20140371570A1
公开(公告)日：2014-12-18
申请号：US13915876
申请日：2013-06-12
Applicant: General Electric Company
Inventor： Cynthia Elizabeth Landberg Davis , Xin Wang , Tzu-Jen Kao , Megan Pearl Rothney
CPC classification number: A61B5/4244 , A61B5/4872 , A61B6/469 , A61B6/482 , A61B6/50 , A61B6/5217
Abstract: Methods for measuring liver fat mass are provided. One method includes acquiring dual-energy two-dimensional (2D) scan information from a dual-energy X-ray scan of a body and generating a dual-energy X-ray image of the body using the 2D scan information. The method further includes identifying a region of interest using the dual-energy X-ray image and determining a subcutaneous fat mass for each of a plurality of sections of the region of interest. The method also includes determining a liver fat mass for the region of interest based on the determined subcutaneous fat mass for each of the plurality of sections.
Abstract translation: 提供了测量肝脏脂肪量的方法。 一种方法包括从身体的双能量X射线扫描获取双能量二维（2D）扫描信息，并使用2D扫描信息产生身体的双能量X射线图像。 该方法还包括使用双能量X射线图像识别感兴趣区域，并且确定感兴趣区域的多个部分中的每个部分的皮下脂肪量。 该方法还包括基于确定的多个部分中的每一个的确定的皮下脂肪量确定感兴趣区域的肝脏脂肪量。
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10.发明授权公开(公告)号：US10074038B2
公开(公告)日：2018-09-11
申请号：US15360742
申请日：2016-11-23
Applicant: General Electric Company
Inventor： Jiang Hsieh , Gopal Avinash , Saad Sirohey , Xin Wang , Zhye Yin , Bruno De Man
CPC classification number: G06K9/6265 , G06K9/036 , G06K9/4604 , G06K9/4628 , G06N3/04 , G06N3/0454 , G06N3/08 , G06N3/084 , G06T7/0002 , G06T7/0012 , G06T2207/10081 , G06T2207/10088 , G06T2207/10104 , G06T2207/10108 , G06T2207/10116 , G06T2207/10132 , G06T2207/30168
Abstract: Methods and apparatus to automatically generate an image quality metric for an image are provided. An example method includes automatically processing a first medical image using a deployed learning network model to generate an image quality metric for the first medical image, the deployed learning network model generated from a digital learning and improvement factory including a training network, wherein the training network is tuned using a set of labeled reference medical images of a plurality of image types, and wherein a label associated with each of the labeled reference medical images indicates a central tendency metric associated with image quality of the image. The example method includes computing the image quality metric associated with the first medical image using the deployed learning network model by leveraging labels and associated central tendency metrics to determine the associated image quality metric for the first medical image.
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