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公开(公告)号：US20190340792A1
公开(公告)日：2019-11-07
申请号：US16511201
申请日：2019-07-15
Applicant: General Electric Company
Inventor： Roshni Bhagalia , Debashish Pal
Abstract: As set forth herein low energy signal data and high energy signal data can be acquired. A first material decomposed (MD) image of a first material basis and a second material decomposed (MD) image of a second material basis can be obtained using the low energy signal data and the high energy signal data. At least one of the first or second MD image can be input into a guide filter for output of at least one noise reduced and cross-contamination reduced image. A computed tomography (CT) imaging system can be provided that includes an X-ray source and a detector having a plurality of detector elements that detect X-ray beams emitted from the X-ray source. Low energy signal data and high energy signal data can be acquired using the detector.
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公开(公告)号：US10573028B2
公开(公告)日：2020-02-25
申请号：US16511201
申请日：2019-07-15
Applicant: General Electric Company
Inventor： Roshni Bhagalia , Debashish Pal
Abstract: As set forth herein low energy signal data and high energy signal data can be acquired. A first material decomposed (MD) image of a first material basis and a second material decomposed (MD) image of a second material basis can be obtained using the low energy signal data and the high energy signal data. At least one of the first or second MD image can be input into a guide filter for output of at least one noise reduced and cross-contamination reduced image. A computed tomography (CT) imaging system can be provided that includes an X-ray source and a detector having a plurality of detector elements that detect X-ray beams emitted from the X-ray source. Low energy signal data and high energy signal data can be acquired using the detector.
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Patent Agency Ranking
公开(公告)号：US20180122082A1
公开(公告)日：2018-05-03
申请号：US15367275
申请日：2016-12-02
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Suvadip Mukherjee , Roshni Bhagalia , Xiaojie Huang
CPC classification number: G06T7/143 , G06N3/04 , G06N3/0454 , G06N3/0472 , G06N3/08 , G06N7/005 , G06T5/002 , G06T7/11 , G06T7/12 , G06T7/136 , G06T7/162 , G06T7/194 , G06T2207/10072 , G06T2207/10081 , G06T2207/10136 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/20161 , G06T2207/30064 , G06T2207/30096 , G06T2207/30101
Abstract: Embodiments described herein provide a hybrid technique which incorporates learned pulmonary nodule features in a model based energy minimization segmentation using graph cuts. Features are extracted from training samples using a convolutional neural network, and the segmentation cost function is augmented via the deep learned energy. The system and method improves segmentation performance and more robust initialization.
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公开(公告)号：US09852501B2
公开(公告)日：2017-12-26
申请号：US15161679
申请日：2016-05-23
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Mirabela Rusu , Roshni Bhagalia
CPC classification number: A61B6/50 , A61B6/032 , G06K9/6228 , G06K9/628 , G06T7/0012 , G06T7/41 , G06T2207/10081 , G06T2207/20076 , G06T2207/20081 , G06T2207/30061
Abstract: A method of creating a diagnostic evaluation for usual interstitial pneumonia is provided, including obtaining a first plurality of series of HRCT lung slices indicating the presence of UIP, obtaining an identification of UIP and non-UIP voxels, extracting textural and localization features from the UIP and non-UIP voxels, selecting features that are more accurate in differentiating UIP voxels from non-UIP voxels than other features are, eliminating features highly correlated with a more accurate feature, and constructing a predictive model by performing a second classifier to provide a probability that a voxel signifies the presence of UIP. Also provided is a method of identifying UIP in a subject's lung by applying a diagnostic evaluation for UIP that was created with the foregoing method.
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公开(公告)号：US10453200B2
公开(公告)日：2019-10-22
申请号：US15367275
申请日：2016-12-02
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Suvadip Mukherjee , Roshni Bhagalia , Xiaojie Huang
IPC: G06T7/143 , G06N3/08 , G06N3/04 , G06N7/00 , G06T7/194 , G06T5/00 , G06T7/11 , G06T7/12 , G06T7/162 , G06T7/136
Abstract: Embodiments described herein provide a hybrid technique which incorporates learned pulmonary nodule features in a model based energy minimization segmentation using graph cuts. Features are extracted from training samples using a convolutional neural network, and the segmentation cost function is augmented via the deep learned energy. The system and method improves segmentation performance and more robust initialization.
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公开(公告)号：US10403006B2
公开(公告)日：2019-09-03
申请号：US15248119
申请日：2016-08-26
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Roshni Bhagalia , Debashish Pal
Abstract: As set forth herein low energy signal data and high energy signal data can be acquired. A first material decomposed (MD) image of a first material basis and a second material decomposed (MD) image of a second material basis can be obtained using the low energy signal data and the high energy signal data. At least one of the first or second MD image can be input into a guide filter for output of at least one noise reduced and cross-contamination reduced image. A computed tomography (CT) imaging system can be provided that includes an X-ray source and a detector having a plurality of detector elements that detect X-ray beams emitted from the X-ray source. Low energy signal data and high energy signal data can be acquired using the detector.
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公开(公告)号：US20150086101A1
公开(公告)日：2015-03-26
申请号：US14496639
申请日：2014-09-25
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Roshni Bhagalia , Qi Song , Albert Amos Montillo
CPC classification number: A61B6/5241 , A61B6/032 , A61B6/463 , A61B6/469 , A61B6/488 , A61B6/503 , A61B6/542 , G06K2009/4666 , G06K2209/051
Abstract: In some embodiments, a method for localizing organs in anatomical imaging may include: performing an anterior-posterior view scan and a lateral view scan to create an anterior-posterior view scan image and a lateral view scan image; creating a joint anatomical model based on the anterior-posterior scan image and the lateral view scan image; and refining the joint anatomical model.
Abstract translation: 在一些实施例中，用于在解剖学成像中定位器官的方法可以包括：执行前 - 后视图扫描和侧视图扫描以创建前 - 后视图扫描图像和侧视图扫描图像; 基于前后扫描图像和侧视图扫描图像创建关节解剖模型; 并完善关节解剖模型。
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