-
11.发明授权公开(公告)号：US09087619B2
公开(公告)日：2015-07-21
申请号：US14451504
申请日：2014-08-05
Applicant: General Electric Company
Inventor： Jiang Hsieh , Thomas Murray Leeds , Abdelaziz Ikhlef
IPC: G21K1/04 , G21K1/02 , G02B27/30 , H01L27/146 , H01L27/148 , A61B6/03 , A61N5/10 , A61B6/00 , G02B27/10
CPC classification number: G21K1/02 , A61B6/03 , A61B6/032 , A61B6/4014 , A61B6/4291 , A61B6/482 , A61B6/583 , A61N5/10 , G02B27/10 , G02B27/30 , G21K1/025 , G21K1/04 , H01L27/14625 , H01L27/14818 , Y10T29/49826
Abstract: A collimator for an imaging system includes a first region comprising a first one-dimensional array of apertures along a channel direction, and a second region comprising a second one-dimensional array of apertures along the channel direction, wherein an aspect ratio of the apertures of the first region is greater than an aspect ratio of the second region.
Abstract translation: 用于成像系统的准直器包括第一区域，其包括沿通道方向的孔的第一一维阵列，以及包括沿着通道方向的孔的第二一维阵列的第二区域，其中孔的纵横比 第一区域大于第二区域的纵横比。
-
12.发明申请SYSTEM AND METHOD OF COMPUTED TOMOGRAPHY SIGNAL RESTORATION VIA NOISE REDUCTION 有权通过噪声减少计算的图像信号恢复的系统和方法公开(公告)号：US20150170341A1
公开(公告)日：2015-06-18
申请号：US14132321
申请日：2013-12-18
Applicant: General Electric Company
Inventor： Jiahua Fan , Meghan L. Yue , Jiang Hsieh , Roman Melnyk , Masatake Nukui , Yujiro Yazaki
CPC classification number: A61B6/5258 , A61B6/03 , A61B6/032 , A61B6/482 , A61B6/5205 , A61B6/582 , G01N2223/401 , G01N2223/419 , G06T5/002 , G06T11/005 , G06T2207/10081 , G06T2207/20182
Abstract: An imaging system includes a computer programmed to estimate noise in computed tomography (CT) imaging data, correlate the noise estimation with neighboring CT imaging data to generate a weighting estimation based on the correlation, de-noise the CT imaging data based on the noise estimation and on the weighting, and reconstruct an image using the de-noised CT imaging data.
Abstract translation: 成像系统包括被编程为估计计算机断层摄影（CT）成像数据中的噪声的计算机，将噪声估计与相邻的CT成像数据相关联，以基于相关性生成加权估计，基于噪声估计对CT成像数据进行去噪 并且在加权时，使用去噪的CT成像数据来重构图像。
-
公开(公告)号：US20140161340A1
公开(公告)日：2014-06-12
申请号：US14181025
申请日：2014-02-14
Inventor： Kai Zeng , Charles Addison Bouman, JR. , Bruno Kristiaan Bernard De Man , Jiang Hsieh , Ken David Sauer , Jean-Baptiste Thibault , Zhou Yu
CPC classification number: A61B6/5258 , G06T11/006 , G06T11/008 , G06T2211/424
Abstract: An improved iterative reconstruction method to reconstruct a first image includes generating an imaging beam, receiving said imaging beam on a detector array, generating projection data based on said imaging beams received by said detector array, providing said projection data to an image reconstructor, enlarging one of a plurality of voxels and a plurality of detectors of the provided projection data, reconstructing portions of the first image with the plurality of enlarged voxels or detectors, and iteratively reconstructing the portions of the first image to create a reconstructed image.
Abstract translation: 用于重建第一图像的改进的迭代重建方法包括产生成像光束，在检测器阵列上接收所述成像光束，基于由所述检测器阵列接收的所述成像光束产生投影数据，将所述投影数据提供给图像重建器， 的多个体素和所提供的投影数据的多个检测器，用多个放大体素或检测器重构第一图像的部分，并迭代地重构第一图像的部分以创建重建图像。
-
公开(公告)号：US11158095B2
公开(公告)日：2021-10-26
申请号：US16112091
申请日：2018-08-24
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Jiang Hsieh
Abstract: A system for reducing artifact bloom in a reconstructed image of an object is provided. The system includes an imaging device, and a controller. The imaging device is operative to obtain one or more slices of the object. The controller is in electronic communication with the imaging device and operative to: generate the reconstructed image based at least in part on the one or more slices; and de-bloom one or more regions within the reconstructed image based at least in part on a contrast medium enhancement across at least part of a volume of the object.
-
公开(公告)号：US11039805B2
公开(公告)日：2021-06-22
申请号：US16474861
申请日：2017-01-05
Applicant: General Electric Company
Inventor： Bruno Kristiaan Bernard De Man , Bernhard Erich Hermann Claus , Jiang Hsieh , Yannan Jin , Zhanfeng Xing
IPC: G06K9/00 , A61B6/00 , G06N20/20 , G06N3/04 , G06N3/08 , G06T3/40 , G06T5/00 , G06T5/20 , G06T5/40 , G06T5/50 , G06T7/00 , G06T11/00
Abstract: A method relates to the use of deep learning techniques, which may be implemented using trained neural networks (50), to estimate various types of missing projection or other unreconstructed data. Similarly, the method may also be employed to replace or correct corrupted or erroneous projection data as opposed to estimating missing projection data.
-
Patent Agency Ranking
16.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.
-
17.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.
-
18.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.
-
公开(公告)号：US20190108441A1
公开(公告)日：2019-04-11
申请号：US15730543
申请日：2017-10-11
Applicant: General Electric Company , Purdue University , Notre Dame University
Inventor： Jean-Baptiste Thibault , Somesh Srivastava , Jiang Hsieh , Charles A. Bouman, JR. , Dong Ye , Ken Sauer
Abstract: The present approach relates to the training of a machine learning algorithm for image generation and use of such a trained algorithm for image generation. Training the machine learning algorithm may involve using multiple images produced from a single set of tomographic projection or image data (such as a simple reconstruction and a computationally intensive reconstruction), where one image is the target image that exhibits the desired characteristics for the final result. The trained machine learning algorithm may be used to generate a final image corresponding to a computationally intensive algorithm from an input image generated using a less computationally intensive algorithm.
-
公开(公告)号：US20180350081A1
公开(公告)日：2018-12-06
申请号：US15607806
申请日：2017-05-30
Applicant: General Electric Company
Inventor： Jiang Hsieh
CPC classification number: G06T7/248 , A61B5/0064 , A61B5/055 , A61B5/1121 , A61B5/1122 , A61B5/1128 , A61B5/113 , A61B5/721 , A61B5/7221 , A61B6/032 , A61B6/037 , A61B6/102 , A61B6/469 , A61B6/5205 , A61B6/527 , A61B2505/05 , G06T7/0016 , G06T7/11 , G06T11/005 , G06T2207/10081 , G06T2207/10088 , G06T2207/20081
Abstract: Systems, apparatuses, and/or methods to provide motion-gated medical imaging. An apparatus may identify a data capture range of a sensor device that is to capture motion of an object during a scan process by a medical imaging device. An apparatus may identify a prescribed scan range. An apparatus may focus motion detection to a region of interest in the data capture range based on the prescribed scan range.
-
-
-
-
-
-
-
-
-
Search Results
73
records
(took 0.028 seconds)
