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公开(公告)号：US20200341100A1
公开(公告)日：2020-10-29
申请号：US16394791
申请日：2019-04-25
Applicant: General Electric Company
Inventor： Isabelle Heukensfeldt Jansen , Sangtae Ahn , Christopher Judson Hardy , Itzik Malkiel , Rafael Shmuel Brada , Ron Wein , Michael Rotman
Abstract: A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.
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公开(公告)号：US10475214B2
公开(公告)日：2019-11-12
申请号：US15480172
申请日：2017-04-05
Applicant: General Electric Company
Inventor： Lin Fu , Sathish Ramani , Bruno Kristiaan Bernard De Man , Xue Rui , Sangtae Ahn
Abstract: The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and/or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.
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Patent Agency Ranking
公开(公告)号：US20180293762A1
公开(公告)日：2018-10-11
申请号：US15480172
申请日：2017-04-05
Applicant: General Electric Company
Inventor： Lin Fu , Sathish Ramani , Bruno Kristiaan Bernard De Man , Xue Rui , Sangtae Ahn
CPC classification number: G06T11/003 , G06K9/66 , G06T11/006 , G06T2210/41 , G06T2211/416
Abstract: The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and/or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.
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公开(公告)号：US09747701B2
公开(公告)日：2017-08-29
申请号：US14831568
申请日：2015-08-20
Applicant: General Electric Company
Inventor： Sangtae Ahn , Ravindra Mohan Manjeshwar , Floribertus P M Heukensfeldt Jansen , Steven Gerard Ross
CPC classification number: G06T11/003 , A61B6/037 , A61B6/469 , A61B6/5217 , G06T7/0012 , G06T7/73 , G06T11/006 , G06T2207/10104 , G06T2207/20104 , G06T2207/30096 , G06T2210/41 , G06T2211/424
Abstract: A method includes acquiring scan data for an object to be imaged using an imaging scanner. The method also includes reconstructing a display image using the scan data. Further, the method includes determining one or more aspects of a quantitation imaging algorithm for generating a quantitation image, wherein the one or more aspects of the quantitation imaging algorithm are selected to optimize a quantitation figure of merit for lesion quantitation. The method also includes reconstructing a quantitation image using the scan data and the quantitation imaging algorithm; displaying, on a display device, the display image; determining a region of interest in the display image; determining, for the region of interest, a lesion quantitation value using a corresponding region of interest of the quantitation image; and displaying, on the display device, the lesion quantitation value.
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公开(公告)号：US20170053423A1
公开(公告)日：2017-02-23
申请号：US14831568
申请日：2015-08-20
Applicant: General Electric Company
Inventor： Sangtae Ahn , Ravindra Mohan Manjeshwar , Floribertus PM Heukensfeldt Jansen , Steven Gerard Ross
CPC classification number: G06T11/003 , A61B6/037 , A61B6/469 , A61B6/5217 , G06T7/0012 , G06T7/73 , G06T11/006 , G06T2207/10104 , G06T2207/20104 , G06T2207/30096 , G06T2210/41 , G06T2211/424
Abstract: A method includes acquiring scan data for an object to be imaged using an imaging scanner. The method also includes reconstructing a display image using the scan data. Further, the method includes determining one or more aspects of a quantitation imaging algorithm for generating a quantitation image, wherein the one or more aspects of the quantitation imaging algorithm are selected to optimize a quantitation figure of merit for lesion quantitation. The method also includes reconstructing a quantitation image using the scan data and the quantitation imaging algorithm; displaying, on a display device, the display image; determining a region of interest in the display image; determining, for the region of interest, a lesion quantitation value using a corresponding region of interest of the quantitation image; and displaying, on the display device, the lesion quantitation value.
Abstract translation: 一种方法包括使用成像扫描器获取要被成像的对象的扫描数据。 该方法还包括使用扫描数据重建显示图像。 此外，该方法包括确定用于产生定量图像的定量成像算法的一个或多个方面，其中选择定量成像算法的一个或多个方面以优化用于病变定量的定量品质因数。 该方法还包括使用扫描数据和定量成像算法重建定量图像; 在显示装置上显示所述显示图像; 确定所述显示图像中的感兴趣区域; 使用所述定量图像的相关感兴趣区域来确定感兴趣区域的病变定量值; 并在显示装置上显示病变定量值。
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26.发明申请JOINT RECONSTRUCTION OF ACTIVITY AND ATTENUATION IN EMISSION TOMOGRAPHY USING MAGNETIC-RESONANCE-BASED PRIORS 审中-公开使用基于磁共振的前列腺联合重建排放反应的活动和衰减公开(公告)号：US20160327622A1
公开(公告)日：2016-11-10
申请号：US14847643
申请日：2015-09-08
Applicant: General Electric Company
Inventor： Sangtae Ahn , Ravindra Mohan Manjeshwar , Florian Wiesinger , Lishui Cheng
CPC classification number: G01R33/481 , A61B5/0035 , A61B5/055 , G01T1/1642 , G01T1/2985 , G01T1/2992 , G06T11/006 , G06T2211/424
Abstract: According to some embodiments, emission projection data and second source scan data are received. A prior map and a prior weight map are generated from second source scan data. A penalty function calculates voxel-wise differences between the prior map and a given image, transforms the voxel-wise differences and calculates a weighted sum of the transformed differences, using weights based on the prior weight map. Joint reconstruction of an emission image and an attenuation map proceeds iteratively and uses the penalty function.
Abstract translation: 根据一些实施例，接收发射投影数据和第二源扫描数据。 从第二源扫描数据生成先验地图和先验权重图。 惩罚函数计算先前地图和给定图像之间的体元差异，转换体元差异，并使用基于先前权重图的权重来计算变换差值的加权和。 发射图像和衰减图的联合重建进行迭代，并使用惩罚函数。
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27.发明授权System and method reconstructing a nuclear medicine image using deformed attenuation image 有权使用变形衰减图像重建核医学图像的系统和方法公开(公告)号：US09031300B1
公开(公告)日：2015-05-12
申请号：US14063473
申请日：2013-10-25
Applicant: General Electric Company
Inventor： Ravindra Mohan Manjeshwar , Sangtae Ahn , Kris Thielemans
CPC classification number: G06T11/003 , G06T11/005
Abstract: According to some embodiments, an emission tomography scanner may acquire emission scan data. One or more anatomical images may be generated using an anatomical imaging system, and the anatomical images may be processed to obtain an initial attenuation image. An emission image and a corrected attenuation image may be jointly reconstructed from the acquired emission scan data, the corrected attenuation image representing a deformation of the initial attenuation image. A final reconstructed emission image may then be calculated based on the reconstructed emission image and/or the corrected attenuation image. The final reconstructed emission image may then be stored in a data storage system and/or displayed on a display system.
Abstract translation: 根据一些实施例，发射断层扫描仪可以获取发射扫描数据。 可以使用解剖学成像系统生成一个或多个解剖图像，并且可以处理解剖图像以获得初始衰减图像。 发射图像和经校正的衰减图像可以从获取的发射扫描数据共同重建，校正的衰减图像表示初始衰减图像的变形。 然后可以基于重构的发射图像和/或校正的衰减图像来计算最终重建的发射图像。 然后可以将最终重构的发射图像存储在数据存储系统中和/或显示在显示系统上。
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