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公开(公告)号：US09147258B2
公开(公告)日：2015-09-29
申请号：US13777981
申请日：2013-02-26
Applicant: General Electric Company
Inventor： Sheshadri Thiruvenkadam , Navneeth Subamanian , Mithun Das Gupta
CPC classification number: G06T7/0097 , G06T7/0012 , G06T7/12 , G06T7/149 , G06T7/174 , G06T2207/10016 , G06T2207/10132 , G06T2207/20116 , G06T2207/30048
Abstract: Methods and systems for segmentation in echocardiography are provided. One method includes obtaining echocardiographic images and defining a search space within the echocardiographic images using a pair of one-dimensional (1D) profiles. The method also includes using an energy based function constrained by non-local temporal priors within the defined search space to automatically segment a contour of a cardiac structure with the 1D profiles.
Abstract translation: 提供超声心动图分割的方法和系统。 一种方法包括获得超声心动图像并使用一对一维（1D）轮廓在超声心动图中定义搜索空间。 该方法还包括使用由限定的搜索空间内的非局部时间先验约束的基于能量的功能，以利用1D轮廓自动分割心脏结构的轮廓。
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公开(公告)号：US11017269B2
公开(公告)日：2021-05-25
申请号：US16334091
申请日：2017-06-21
Applicant: General Electric Company
Inventor： Sheshadri Thiruvenkadam , Sohan Rashmi Ranjan , Vivek Prabhakar Vaidya , Hariharan Ravishankar , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for determining optimized deep learning architecture includes receiving a plurality of training images and a plurality of real time images corresponding to a subject. The method further includes receiving, by a medical practitioner, a plurality of learning parameters comprising a plurality of filter classes and a plurality of architecture parameters. The method also includes determining a deep learning model based on the plurality of learning parameters and the plurality of training images, wherein the deep learning model comprises a plurality of reusable filters. The method further includes determining a health condition of the subject based on the plurality of real time images and the deep learning model. The method also includes providing the health condition of the subject to the medical practitioner.
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公开(公告)号：US10436858B2
公开(公告)日：2019-10-08
申请号：US15532680
申请日：2015-12-02
Applicant: General Electric Company
Inventor： Dattesh Dayanand Shanbhag , Rakesh Mullick , Sheshadri Thiruvenkadam , Florian Wiesinger , Sudhanya Chatterjee , Kevin Matthew Koch
Abstract: An imaging system and method are disclosed. An MR image and measured B0 field map of a target volume in a subject are reconstructed, where the MR image includes one or more bright and/or dark regions. One or more distinctive constituent materials corresponding to the bright regions are identified. Each dark region is iteratively labeled as one or more ambiguous constituent materials. Susceptibility values corresponding to each distinctive and iteratively labeled ambiguous constituent material is assigned. A simulated B0 field map is iteratively generated based on the assigned susceptibility values. A similarity metric is determined between the measured and simulated B0 field maps. Constituent materials are identified in the dark regions based on the similarity metric to ascertain corresponding susceptibility values. The MRI data is corrected based on the assigned and ascertained susceptibility values. A diagnostic assessment of the target volume is determined based on the corrected MRI data.
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公开(公告)号：US11810301B2
公开(公告)日：2023-11-07
申请号：US17227093
申请日：2021-04-09
Applicant: General Electric Company
Inventor： Harihan Ravishankar , Vivek Vaidya , Sheshadri Thiruvenkadam , Rahul Venkataramani , Prasad Sudhakar
CPC classification number: G06T7/10 , G06F17/15 , G06N3/045 , G06T2207/20084
Abstract: A method for image segmentation includes receiving an input image. The method further includes obtaining a deep learning model having a triad of predictors. Furthermore, the method includes processing the input image by a shape model in the triad of predictors to generate a segmented shape image. Moreover, the method includes presenting the segmented shape image via a display unit.
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公开(公告)号：US09471976B2
公开(公告)日：2016-10-18
申请号：US14626976
申请日：2015-02-20
Applicant: General Electric Company , King's College London
Inventor： Sheshadri Thiruvenkadam , Krishna Seetharam Shriram , Ravindra Mohan Manjeshwar , Srikrishnan Viswanathan , Kris Filip Johan Jules Thielemans
CPC classification number: G06T7/0012 , A61B6/037 , A61B6/5217 , A61B6/5241 , A61B6/5264 , A61B6/5288 , G06T7/20 , G06T7/262 , G06T2207/10104 , G06T2207/30004
Abstract: A method implemented using at least one processor includes receiving time-varying image dataset generated by a medical imaging modality. The image dataset corresponds to a bed position and is affected by quasi-periodic motion data. The method also includes applying a signal decomposition technique to the time-varying image dataset to generate a plurality of dataset components and a plurality of motion signals. The method also includes determining reference data based on the time-varying image dataset, wherein the reference data is representative of a direction of the quasi-periodic motion. The method further includes deriving polarity of each of the plurality of motion signals based on the reference data to generate a plurality of sign corrected motion signals. The method also includes determining a gating signal corresponding to the bed position based on at least one of the plurality of sign corrected motion signals.
Abstract translation: 使用至少一个处理器实现的方法包括接收由医学成像模式产生的时变图像数据集。 图像数据集对应于床位置并受准周期运动数据的影响。 该方法还包括将信号分解技术应用于时变图像数据集以产生多个数据集分量和多个运动信号。 该方法还包括基于时变图像数据集确定参考数据，其中参考数据表示准周期运动的方向。 该方法还包括基于参考数据导出多个运动信号中的每一个的极性，以产生多个符号校正运动信号。 该方法还包括基于多个符号校正的运动信号中的至少一个确定与床位置对应的选通信号。
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Patent Agency Ranking
公开(公告)号：US20160247274A1
公开(公告)日：2016-08-25
申请号：US14626976
申请日：2015-02-20
Applicant: General Electric Company , King's College London
Inventor： Sheshadri Thiruvenkadam , Krishna Seetharam Shriram , Ravindra Mohan Manjeshwar , Srikrishnan Viswanathan , Kris Filip Johan Jules Thielemans
CPC classification number: G06T7/0012 , A61B6/037 , A61B6/5217 , A61B6/5241 , A61B6/5264 , A61B6/5288 , G06T7/20 , G06T7/262 , G06T2207/10104 , G06T2207/30004
Abstract: A method implemented using at least one processor includes receiving time-varying image dataset generated by a medical imaging modality. The image dataset corresponds to a bed position and is affected by quasi-periodic motion data. The method also includes applying a signal decomposition technique to the time-varying image dataset to generate a plurality of dataset components and a plurality of motion signals. The method also includes determining reference data based on the time-varying image dataset, wherein the reference data is representative of a direction of the quasi-periodic motion. The method further includes deriving polarity of each of the plurality of motion signals based on the reference data to generate a plurality of sign corrected motion signals. The method also includes determining a gating signal corresponding to the bed position based on at least one of the plurality of sign corrected motion signals.
Abstract translation: 使用至少一个处理器实现的方法包括接收由医学成像模式产生的时变图像数据集。 图像数据集对应于床位置并受准周期运动数据的影响。 该方法还包括将信号分解技术应用于时变图像数据集以产生多个数据集分量和多个运动信号。 该方法还包括基于时变图像数据集确定参考数据，其中参考数据代表准周期运动的方向。 该方法还包括基于参考数据导出多个运动信号中的每一个的极性，以产生多个符号校正运动信号。 该方法还包括基于多个符号校正的运动信号中的至少一个确定与床位置对应的选通信号。
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公开(公告)号：US20170371010A1
公开(公告)日：2017-12-28
申请号：US15532680
申请日：2015-12-02
Applicant: General Electric Company
Inventor： Dattesh Dayanand Shanbhag , Rakesh Mullick , Sheshadri Thiruvenkadam , Florian Wiesinger , Sudhanya Chatterjee , Kevin Matthew Koch
CPC classification number: G01R33/243 , A61B5/0035 , A61B5/055 , G01R33/481 , G01R33/4816 , G01R33/5608 , G01R33/56536
Abstract: An imaging system and method are disclosed. An MR image and measured B0 field map of a target volume in a subject are reconstructed, where the MR image includes one or more bright and/or dark regions. One or more distinctive constituent materials corresponding to the bright regions are identified. Each dark region is iteratively labeled as one or more ambiguous constituent materials. Susceptibility values corresponding to each distinctive and iteratively labeled ambiguous constituent material is assigned. A simulated B0 field map is iteratively generated based on the assigned susceptibility values. A similarity metric is determined between the measured and simulated B0 field maps. Constituent materials are identified in the dark regions based on the similarity metric to ascertain corresponding susceptibility values. The MRI data is corrected based on the assigned and ascertained susceptibility values. A diagnostic assessment of the target volume is determined based on the corrected MRI data.
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8.发明授权Methods and systems for generating a positron emission tomography attenuation correction map 有权用于产生正电子发射断层扫描衰减校正图的方法和系统公开(公告)号：US08867814B2
公开(公告)日：2014-10-21
申请号：US13644916
申请日：2012-10-04
Applicant: General Electric Company
Inventor： Albert Henry Roger Lonn , Scott David Wollenweber , Dattesh Dayanand Shanbhag , Sheshadri Thiruvenkadam
IPC: G06K9/00
CPC classification number: G06T11/005
Abstract: A method for generating a positron emission tomography (PET) attenuation correction map. The method includes obtaining a magnetic resonance (MR) image dataset of a subject of interest, obtaining a positron emission tomography (PET) emission dataset of the subject of interest, segmenting the MR image dataset to identify at least one object of interest, determining a volume of the object of interest, and generating a PET attenuation correction map using the determined volume. A medical imaging system and a non-transitory computer readable medium are also described herein.
Abstract translation: 一种用于产生正电子发射断层摄影（PET）衰减校正图的方法。 该方法包括获得感兴趣对象的磁共振（MR）图像数据集，获得感兴趣对象的正电子发射断层摄影（PET）发射数据集，分割MR图像数据集以识别至少一个感兴趣对象，确定 感兴趣对象的体积，以及使用确定的体积产生PET衰减校正图。 本文还描述了医学成像系统和非暂时性计算机可读介质。
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9.发明申请METHODS AND SYSTEMS FOR GENERATING A POSITRON EMISSION TOMOGRAPHY ATTENUATION CORRECTION MAP 有权用于产生位置发射TOMOGRAPHY衰减校正图的方法和系统公开(公告)号：US20140099009A1
公开(公告)日：2014-04-10
申请号：US13644916
申请日：2012-10-04
Applicant: GENERAL ELECTRIC COMPANY
Inventor： Albert Henry Roger Lonn , Scott David Wollenweber , Dattesh Dayanand Shanbhag , Sheshadri Thiruvenkadam
IPC: G06K9/34
CPC classification number: G06T11/005
Abstract: A method for generating a positron emission tomography (PET) attenuation correction map. The method includes obtaining a magnetic resonance (MR) image dataset of a subject of interest, obtaining a positron emission tomography (PET) emission dataset of the subject of interest, segmenting the MR image dataset to identify at least one object of interest, determining a volume of the object of interest, and generating a PET attenuation correction map using the determined volume. A medical imaging system and a non-transitory computer readable medium are also described herein
Abstract translation: 一种用于产生正电子发射断层摄影（PET）衰减校正图的方法。 该方法包括获得感兴趣对象的磁共振（MR）图像数据集，获得感兴趣对象的正电子发射断层摄影（PET）发射数据集，分割MR图像数据集以识别至少一个感兴趣对象，确定 感兴趣对象的体积，以及使用确定的体积产生PET衰减校正图。 本文还描述了医学成像系统和非暂时性计算机可读介质
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公开(公告)号：US20210233244A1
公开(公告)日：2021-07-29
申请号：US17227093
申请日：2021-04-09
Applicant: General Electric Company
Inventor： Harihan Ravishankar , Vivek Vaidya , Sheshadri Thiruvenkadam , Rahul Venkataramani , Prasad Sudhakar
Abstract: A method for image segmentation includes receiving an input image. The method further includes obtaining a deep learning model having a triad of predictors. Furthermore, the method includes processing the input image by a shape model in the triad of predictors to generate a segmented shape image. Moreover, the method includes presenting the segmented shape image via a display unit.
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