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公开(公告)号：US20170221235A1
公开(公告)日：2017-08-03
申请号：US15012556
申请日：2016-02-01
Applicant: General Electric Company
Inventor： Bruno Kristiaan Bernard De Man , Lin Fu , Jiajia Luo , Eri Haneda , Sathish Ramani
CPC classification number: G06T11/008 , G06K9/4628 , G06K9/6255 , G06T5/002 , G06T2207/10081 , G06T2207/20081 , G06T2211/424
Abstract: The present approach relates to the use of a database (i.e., a dictionary) of image patterns to be avoided or de-emphasized during an image reconstruction process, such as an iterative image reconstruction process. Such a dictionary may be characterized as a negative or “bad” dictionary. The negative dictionary may be used to constrain an image reconstruction process to avoid or minimize the presence of the patterns present in the negative dictionary.
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公开(公告)号：US20170039735A1
公开(公告)日：2017-02-09
申请号：US14820293
申请日：2015-08-06
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Ali Can , Bruno Kristiaan Bernard De Man , Jed Douglas Pack , John Christopher Boot , Eri Haneda
CPC classification number: G01N23/046 , G01N33/24 , G01N2223/419 , G01N2223/616 , G06T11/005
Abstract: Approaches related to performing calibration of a CT scanner or of processes (e.g., correction and/or reconstruction) performed on acquired CT scan data are described. In certain described approaches, calibration is attained without performing a calibration scan using a dedicated calibration phantom. In certain embodiments, calibration is performed using a feature intrinsic to the imaged object, such as a jacket disposed about a drilled core sample.
Abstract translation: 描述与执行CT扫描器的校准或对获取的CT扫描数据执行的处理（例如，校正和/或重构）相关的方法。 在某些描述的方法中，在不使用专用校准体模进行校准扫描的情况下获得校准。 在某些实施例中，使用成像对象固有的特征来执行校准，例如围绕钻孔芯样本设置的护套。
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Patent Agency Ranking
公开(公告)号：US20200163639A1
公开(公告)日：2020-05-28
申请号：US16200175
申请日：2018-11-26
Applicant: General Electric Company
Inventor： Bruno Kristiaan Bernard De Man , Eri Haneda , Jed Douglas Pack , Bernhard Erich Hermann Claus
Abstract: In accordance with the present disclosure, the present technique finds a diagnostic scan timing for a non-static object (e.g., a heart or other dynamic object undergoing motion) from raw scan data, as opposed to reconstructed image data. To find the scan timing, a monitoring scan of a patient's heart is performed. In the monitoring scan, the patient dose may be limited or minimized. As the projection data is acquired during such a monitoring scan, the projection data may be subjected to sinogram analysis in a concurrent or real-time manner to determine when to start (or trigger) the diagnostic scan.
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公开(公告)号：US10448915B2
公开(公告)日：2019-10-22
申请号：US15633819
申请日：2017-06-27
Applicant: General Electric Company
Inventor： Bruno Kristiaan Bernard De Man , Jed Douglas Pack , Eri Haneda , Sathish Ramani , Jiang Hsieh , James Vradenburg Miller , Peter Michael Edic
Abstract: A method for characterizing anatomical features includes receiving scanned data and image data corresponding to a subject. The scanned data comprises sinogram data. The method further includes identifying a first region in an image of the image data corresponding to a region of interest. The method also includes determining a second region in the scanned data. The second region corresponds to the first region. The method further includes identifying a sinogram trace corresponding to the region of interest. The sinogram trace comprises sinogram data present within the second region. The method includes determining a data feature of the subject based on the sinogram trace and a deep learning network. The method also includes determining a diagnostic condition corresponding to a medical condition of the subject based on the data feature.
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公开(公告)号：US09824468B2
公开(公告)日：2017-11-21
申请号：US14985702
申请日：2015-12-31
Applicant: General Electric Company
Inventor： Jiajia Luo , Bruno Kristiaan Bernard De Man , Ali Can , Eri Haneda
CPC classification number: G06T11/006 , G06K9/4642 , G06K9/6244 , G06K9/6255 , G06K9/6256 , G06K2209/051 , G06T11/008 , G06T2210/41 , G06T2211/421 , G06T2211/424
Abstract: A computationally efficient dictionary learning-based term is employed in an iterative reconstruction framework to keep more spatial information than two-dimensional dictionary learning and require less computational cost than three-dimensional dictionary learning. In one such implementation, a non-local regularization algorithm is employed in an MBIR context (such as in a low dose CT image reconstruction context) based on dictionary learning in which dictionaries from different directions (e.g., x,y-plane, y,z-plane, x,z-plane) are employed and the sparse coefficients calculated accordingly. In this manner, spatial information from all three directions is retained and computational cost is constrained.
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公开(公告)号：US10736594B2
公开(公告)日：2020-08-11
申请号：US16200175
申请日：2018-11-26
Applicant: General Electric Company
Inventor： Bruno Kristiaan Bernard De Man , Eri Haneda , Jed Douglas Pack , Bernhard Erich Hermann Claus
Abstract: In accordance with the present disclosure, the present technique finds a diagnostic scan timing for a non-static object (e.g., a heart or other dynamic object undergoing motion) from raw scan data, as opposed to reconstructed image data. To find the scan timing, a monitoring scan of a patient's heart is performed. In the monitoring scan, the patient dose may be limited or minimized. As the projection data is acquired during such a monitoring scan, the projection data may be subjected to sinogram analysis in a concurrent or real-time manner to determine when to start (or trigger) the diagnostic scan.
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公开(公告)号：US20170091964A1
公开(公告)日：2017-03-30
申请号：US14985702
申请日：2015-12-31
Applicant: General Electric Company
Inventor： Jiajia Luo , Bruno Kristiaan Bernard De Man , Ali Can , Eri Haneda
CPC classification number: G06T11/006 , G06K9/4642 , G06K9/6244 , G06K9/6255 , G06K9/6256 , G06K2209/051 , G06T11/008 , G06T2210/41 , G06T2211/421 , G06T2211/424
Abstract: A computationally efficient dictionary learning-based term is employed in an iterative reconstruction framework to keep more spatial information than two-dimensional dictionary learning and require less computational cost than three-dimensional dictionary learning. In one such implementation, a non-local regularization algorithm is employed in an MBIR context (such as in a low dose CT image reconstruction context) based on dictionary learning in which dictionaries from different directions (e.g., x,y-plane, y,z-plane, x,z-plane) are employed and the sparse coefficients calculated accordingly. In this manner, spatial information from all three directions is retained and computational cost is constrained.
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