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公开(公告)号：US20220117570A1
公开(公告)日：2022-04-21
申请号：US17563970
申请日：2021-12-28
Applicant: General Electric Company
Inventor： Mark Vincent Profio , Eric Gros , Christine Carol Hammond , Darin Robert Okerlund
Abstract: Systems and methods are provided for contrast-enhanced diagnostic imaging. In one aspect, a system comprises an x-ray source that emits a beam of x-rays towards a subject to be imaged; a detector that receives the x-rays attenuated by the subject; a data acquisition system (DAS) operably connected to the detector; and a computing device operably connected to the DAS and configured with executable instructions in non-transitory memory that when executed cause the computing device to generate a first estimated time to perform a diagnostic scan of the subject based on demographic information and clinical information of the patient; and control the x-ray source and the detector to perform the diagnostic scan of the subject at the first estimated time responsive to a first confidence level of the first estimated time above a threshold.
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公开(公告)号：US20190231288A1
公开(公告)日：2019-08-01
申请号：US15881526
申请日：2018-01-26
Applicant: General Electric Company
Inventor： Mark Vincent Profio , Eric Gros , Christine Carol Hammond , Darin Robert Okerlund
CPC classification number: A61B6/484 , A61B5/7264 , A61B6/032 , A61B6/0407 , A61B6/40 , A61B6/42 , A61B6/54
Abstract: Methods and systems are provided for contrast-enhanced diagnostic imaging. In one embodiment, a method comprises estimating a time to perform a diagnostic scan of a patient based on demographics of the patient, and performing the diagnostic scan of the patient at the estimated time responsive to a confidence level of the estimated time above a threshold. In this way, a contrast-enhanced diagnostic scan may be performed without directly monitoring the contrast flow, thereby reducing radiation dose and contrast load while maintaining or improving image quality.
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Patent Agency Ranking
3.发明授权公开(公告)号：US10739770B2
公开(公告)日：2020-08-11
申请号：US15872582
申请日：2018-01-16
Applicant: General Electric Company
Inventor： Huan Tan , Ana Dasilva , Eric Gros , Romano Patrick , Charles Theurer , Mauricio Castillo-Effen , John Lizzi
Abstract: Modifying a motion plan for an autonomously-operated inspection platform (AIP) includes obtaining sensor data for an industrial asset area of interest, analyzing the obtained sensor data during execution of an initial motion plan to determine if modification of the initial motion plan is required. If modification is required then performing a pose estimation on a first group of potential targets and a second group of potential targets, optimizing the results of the pose estimation to determine a modification to the initial motion plan, performing reactive planning to the initial motion plan to include the modification, the reactive planning providing a modified motion plan that includes a series of waypoints defining a modified path, and autonomously controlling motion of the AIP along the modified path. The analysis, pose estimation, optimization, and reactive planning occurring during movement of the AIP along a motion plan. A system and computer-readable medium are disclosed.
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公开(公告)号：US10475216B2
公开(公告)日：2019-11-12
申请号：US15905351
申请日：2018-02-26
Applicant: General Electric Company
Inventor： John Irvin Jackson , John Londt , Eric Gros , Dave Chevalier
Abstract: An imaging system determines a heart rate of a patient, a cardiac disease state, and/or a target cardiac anatomy of the patient. The system calculates an image acquisition time range from a patient population model using the heart rate, the cardiac disease state, and/or the target anatomy. The model represents relationships between cardiac motion of other patients and time or cardiac phases of the other patients. The system also determines imaging settings to acquire image data of the target anatomy during the image acquisition time range that is calculated. Imaging the target anatomy of the patient according to the imaging settings generates image data of the target anatomy having less cardiac motion and/or a reduced image acquisition time range relative to determining the imaging settings without using the patient population model.
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公开(公告)号：US20190266760A1
公开(公告)日：2019-08-29
申请号：US15905351
申请日：2018-02-26
Applicant: General Electric Company
Inventor： John Irvin Jackson , John Londt , Eric Gros , Dave Chevalier
Abstract: An imaging system determines a heart rate of a patient, a cardiac disease state, and/or a target cardiac anatomy of the patient. The system calculates an image acquisition time range from a patient population model using the heart rate, the cardiac disease state, and/or the target anatomy. The model represents relationships between cardiac motion of other patients and time or cardiac phases of the other patients. The system also determines imaging settings to acquire image data of the target anatomy during the image acquisition time range that is calculated. Imaging the target anatomy of the patient according to the imaging settings generates image data of the target anatomy having less cardiac motion and/or a reduced image acquisition time range relative to determining the imaging settings without using the patient population model.
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公开(公告)号：US10949951B2
公开(公告)日：2021-03-16
申请号：US16110764
申请日：2018-08-23
Applicant: General Electric Company
Inventor： Jie Tang , Eric Gros , Jiang Hsieh , Roy Nilsen
Abstract: Systems and methods for improved image denoising using a deep learning network model are disclosed. An example system includes an input data processor to process a first patient image of a first patient to add a first noise to the first patient image to form a noisy image input. The example system includes an image data denoiser to process the noisy image input using a first deep learning network to identify the first noise. The image data denoiser is to train the first deep learning network using the noisy image input. When the first deep learning network is trained to identify the first noise, the image data denoiser is to deploy the first deep learning network as a second deep learning network model to be applied to a second patient image of the first patient to identify a second noise in the second patient image.
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公开(公告)号：US20200065940A1
公开(公告)日：2020-02-27
申请号：US16110764
申请日：2018-08-23
Applicant: General Electric Company
Inventor： Jie Tang , Eric Gros , Jiang Hsieh , Roy Nilsen
Abstract: Systems and methods for improved image denoising using a deep learning network model are disclosed. An example system includes an input data processor to process a first patient image of a first patient to add a first noise to the first patient image to form a noisy image input. The example system includes an image data denoiser to process the noisy image input using a first deep learning network to identify the first noise. The image data denoiser is to train the first deep learning network using the noisy image input. When the first deep learning network is trained to identify the first noise, the image data denoiser is to deploy the first deep learning network as a second deep learning network model to be applied to a second patient image of the first patient to identify a second noise in the second patient image.
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