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公开(公告)号：US20160242712A1
公开(公告)日：2016-08-25
申请号：US15044787
申请日：2016-02-16
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Yannan Jin , Bruno Kristiaan Bernard De Man , Peter Michael Edic , Paul Francis Fitzgerald , Xue Rui , Yangyang Yao , Zhye Yin , Uwe Wiedmann
CPC classification number: A61B6/405 , A61B6/032 , A61B6/4233 , A61B6/488 , A61B6/5205 , A61B6/542
Abstract: An imaging method includes executing a low-dose preparatory scan to an object by applying tube voltages and tube currents in an x-ray source, and generating a first image of the object corresponding to the low-dose preparatory scan. The method further includes generating image quality estimates and dose estimates view by view at least based on the first image. The method includes optimizing the tube voltages and the tube currents to generate optimal profiles for the tube voltage and the tube current. At least one of the optimal profiles for the tube voltage and the tube current is generated based on the image quality estimates and the dose estimates. The method includes executing an acquisition scan by applying the tube voltages and the tube currents based on the optimal profiles and generating a second image of the object corresponding to the acquisition scan. An imaging system is also provided.
Abstract translation: 成像方法包括通过在x射线源中施加管电压和管电流来对对象执行低剂量预备扫描，以及生成对应于低剂量预备扫描的对象的第一图像。 该方法还包括至少基于第一图像通过视图生成图像质量估计和剂量估计视图。 该方法包括优化管电压和管电流以产生管电压和管电流的最佳轮廓。 基于图像质量估计和剂量估计，产生管电压和管电流的最佳曲线中的至少一个。 该方法包括通过基于最佳轮廓施加管电压和管电流来执行采集扫描，并且生成对应于采集扫描的对象的第二图像。 还提供了一种成像系统。
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2.发明申请公开(公告)号：US20200097773A1
公开(公告)日：2020-03-26
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 DeMan
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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3.发明申请公开(公告)号：US20190340470A1
公开(公告)日：2019-11-07
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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4.发明授权公开(公告)号：US10354171B2
公开(公告)日：2019-07-16
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
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：US10213172B2
公开(公告)日：2019-02-26
申请号：US15044787
申请日：2016-02-16
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Yannan Jin , Bruno Kristiaan Bernard De Man , Peter Michael Edic , Paul Francis Fitzgerald , Xue Rui , Yangyang Yao , Zhye Yin , Uwe Wiedmann
Abstract: An imaging method includes executing a low-dose preparatory scan to an object by applying tube voltages and tube currents in an x-ray source, and generating a first image of the object corresponding to the low-dose preparatory scan. The method further includes generating image quality estimates and dose estimates view by view at least based on the first image. The method includes optimizing the tube voltages and the tube currents to generate optimal profiles for the tube voltage and the tube current. At least one of the optimal profiles for the tube voltage and the tube current is generated based on the image quality estimates and the dose estimates. The method includes executing an acquisition scan by applying the tube voltages and the tube currents based on the optimal profiles and generating a second image of the object corresponding to the acquisition scan. An imaging system is also provided.
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Patent Agency Ranking
6.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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公开(公告)号：US09042512B2
公开(公告)日：2015-05-26
申请号：US13675875
申请日：2012-11-13
Applicant: General Electric Company
Inventor： Zhye Yin , Bruno Kristiaan Bernard De Man , Jed Douglas Pack , Kyle Morgan Champley , Kai Zeng
CPC classification number: A61B6/503 , A61B6/032 , A61B6/5205 , A61B6/541 , G06F19/00
Abstract: An approach is disclosed for acquiring multi-sector computed tomography scan data. The approach includes activating an X-ray source during heartbeats of a patient to acquire projection data over a limited angular range for each heartbeat. The projection data acquired over the different is combined. An image having good temporal resolution is reconstructed using the combined projection data.
Abstract translation: 公开了一种用于获取多扇区计算机断层摄影扫描数据的方法。 该方法包括在患者的心跳期间激活X射线源以在每个心跳的有限角度范围内获取投影数据。 将不同的投影数据合并。 使用组合的投影数据重建具有良好时间分辨率的图像。
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8.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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9.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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10.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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