公开(公告)号：US08682626B2

公开(公告)日：2014-03-25

申请号：US13091076

申请日：2011-04-20

申请人： Razvan Ioan Ionasec ,  Ingmar Voigt ,  Viorel Mihalef ,  Sasa Grbic ,  Dime Vitanovski ,  Yang Wang ,  Yefeng Zheng ,  Bogdan Georgescu ,  Dorin Comaniciu ,  Puneet Sharma ,  Tommaso Mansi

发明人： Razvan Ioan Ionasec ,  Ingmar Voigt ,  Viorel Mihalef ,  Sasa Grbic ,  Dime Vitanovski ,  Yang Wang ,  Yefeng Zheng ,  Bogdan Georgescu ,  Dorin Comaniciu ,  Puneet Sharma ,  Tommaso Mansi

IPC分类号： G06G7/48

CPC分类号： G06T13/20 ,  G06F19/321 ,  G06T2210/24 ,  G06T2210/41 ,  G16H50/50

摘要： A method and system for patient-specific modeling of the whole heart anatomy, dynamics, hemodynamics, and fluid structure interaction from 4D medical image data is disclosed. The anatomy and dynamics of the heart are determined by estimating patient-specific parameters of a physiological model of the heart from the 4D medical image data for a patient. The patient-specific anatomy and dynamics are used as input to a 3D Navier-Stokes solver that derives realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. Fluid structure interactions are determined iteratively over the heart cycle by simulating the blood flow at a given time step and calculating the deformation of the heart structure based on the simulated blood flow, such that the deformation of the heart structure is used in the simulation of the blood flow at the next time step. The comprehensive patient-specific model of the heart representing anatomy, dynamics, hemodynamics, and fluid structure interaction can be used for non-invasive assessment and diagnosis of the heart, as well as virtual therapy planning and cardiovascular disease management. Parameters of the comprehensive patient-specific model are changed or perturbed to simulate various conditions or treatment options, and then the patient specific model is recalculated to predict the effect of the conditions or treatment options.

摘要翻译： 公开了一种用于针对4D医学图像数据的整个心脏解剖结构，动力学，血流动力学和流体结构相互作用的患者特异性建模的方法和系统。 通过从患者的4D医学图像数据估计心脏的生理模型的患者特异性参数来确定心脏的解剖学和动力学。 患者特异性解剖学和动力学被用作3D Navier-Stokes求解器的输入，该解算器在整个心脏周期中导出由局部解剖结构约束的现实血液动力学。 流体结构相互作用是通过在给定的时间步长模拟血液流动而在心脏周期上迭代地确定的，并且基于模拟的血液流量计算心脏结构的变形，使得心脏结构的变形用于模拟 血液流动在下一个时间步。 代表解剖学，动力学，血液动力学和流体结构相互作用的心脏综合患者特异性模型可用于心脏的非侵入性评估和诊断，以及虚拟治疗计划和心血管疾病管理。 全面的患者特异性模型的参数被改变或扰动以模拟各种条件或治疗选择，然后重新计算患者特异性模型以预测条件或治疗选择的影响。

公开(公告)号：US20120022843A1

公开(公告)日：2012-01-26

申请号：US13091076

申请日：2011-04-20

申请人： Razvan Ioan Ionasec ,  Ingmar Voigt ,  Viorel Mihalef ,  Sasa Grbic ,  Dime Vitanovski ,  Yang Wang ,  Yefeng Zheng ,  Bogdan Georgescu ,  Dorin Comaniciu ,  Puneet Sharma ,  Tommaso Mansi

发明人： Razvan Ioan Ionasec ,  Ingmar Voigt ,  Viorel Mihalef ,  Sasa Grbic ,  Dime Vitanovski ,  Yang Wang ,  Yefeng Zheng ,  Bogdan Georgescu ,  Dorin Comaniciu ,  Puneet Sharma ,  Tommaso Mansi

IPC分类号： G06G7/60 ,  G06G7/57

CPC分类号： G06T13/20 ,  G06F19/321 ,  G06T2210/24 ,  G06T2210/41 ,  G16H50/50

摘要： A method and system for patient-specific modeling of the whole heart anatomy, dynamics, hemodynamics, and fluid structure interaction from 4D medical image data is disclosed. The anatomy and dynamics of the heart are determined by estimating patient-specific parameters of a physiological model of the heart from the 4D medical image data for a patient. The patient-specific anatomy and dynamics are used as input to a 3D Navier-Stokes solver that derives realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. Fluid structure interactions are determined iteratively over the heart cycle by simulating the blood flow at a given time step and calculating the deformation of the heart structure based on the simulated blood flow, such that the deformation of the heart structure is used in the simulation of the blood flow at the next time step. The comprehensive patient-specific model of the heart representing anatomy, dynamics, hemodynamics, and fluid structure interaction can be used for non-invasive assessment and diagnosis of the heart, as well as virtual therapy planning and cardiovascular disease management. Parameters of the comprehensive patient-specific model are changed or perturbed to simulate various conditions or treatment options, and then the patient specific model is recalculated to predict the effect of the conditions or treatment options.

摘要翻译： 公开了一种用于针对4D医学图像数据的整个心脏解剖结构，动力学，血流动力学和流体结构相互作用的患者特异性建模的方法和系统。 通过从患者的4D医学图像数据估计心脏的生理模型的患者特异性参数来确定心脏的解剖学和动力学。 患者特异性解剖学和动力学被用作3D Navier-Stokes求解器的输入，该解算器在整个心脏周期中导出由局部解剖结构约束的现实血液动力学。 流体结构相互作用是通过在给定的时间步长模拟血液流动而在心脏周期上迭代地确定的，并且基于模拟的血液流量计算心脏结构的变形，使得心脏结构的变形用于模拟 血液流动在下一个时间步。 代表解剖学，动力学，血液动力学和流体结构相互作用的心脏综合患者特异性模型可用于心脏的非侵入性评估和诊断，以及虚拟治疗计划和心血管疾病管理。 全面的患者特异性模型的参数被改变或扰动以模拟各种条件或治疗选择，然后重新计算患者特异性模型以预测条件或治疗选择的影响。

公开(公告)号：US09824302B2

公开(公告)日：2017-11-21

申请号：US13412869

申请日：2012-03-06

申请人： Sasa Grbic ,  Razvan Ioan Ionasec ,  Yang Wang ,  Bogdan Georgescu ,  Tommaso Mansi ,  Dorin Comaniciu ,  Yefeng Zheng ,  Shaohua Kevin Zhou ,  Matthias John ,  Jan Boese

发明人： Sasa Grbic ,  Razvan Ioan Ionasec ,  Yang Wang ,  Bogdan Georgescu ,  Tommaso Mansi ,  Dorin Comaniciu ,  Yefeng Zheng ,  Shaohua Kevin Zhou ,  Matthias John ,  Jan Boese

IPC分类号： G06K9/00 ,  G06K9/62

CPC分类号： G06K9/6289 ,  G06K9/6211

摘要： A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.

公开(公告)号：US20120230568A1

公开(公告)日：2012-09-13

申请号：US13412869

申请日：2012-03-06

申请人： Sasa Grbic ,  Razvan Ioan Ionasec ,  Yang Wang ,  Bogdan Georgescu ,  Tommaso Mansi ,  Dorin Comaniciu ,  Yefeng Zheng ,  Shaohua Kevin Zhou ,  Matthias John ,  Jan Boese

发明人： Sasa Grbic ,  Razvan Ioan Ionasec ,  Yang Wang ,  Bogdan Georgescu ,  Tommaso Mansi ,  Dorin Comaniciu ,  Yefeng Zheng ,  Shaohua Kevin Zhou ,  Matthias John ,  Jan Boese

IPC分类号： G06K9/36

CPC分类号： G06K9/6289 ,  G06K9/6211

摘要： A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.

摘要翻译： 公开了一种用于融合多模体积图像的方法和系统。 接收使用第一成像模式获取的第一图像。 接收使用第二成像模态获取的第二图像。 从第一和第二图像共同估计模型和目标解剖结构和变换。 该模型表示第一图像中目标解剖结构的模型，并且变换将第二图像中的目标解剖结构的模型投影到第一图像中的模型。 可以基于估计的变换来融合第一和第二图像。

公开(公告)号：US20140052001A1

公开(公告)日：2014-02-20

申请号：US13905690

申请日：2013-05-30

申请人： Razvan Ioan Ionasec ,  Dime Vitanovski ,  Yang Wang ,  Bogdan Georgescu ,  Ingmar Voigt ,  Saurabh Datta ,  Dorin Comaniciu

发明人： Razvan Ioan Ionasec ,  Dime Vitanovski ,  Yang Wang ,  Bogdan Georgescu ,  Ingmar Voigt ,  Saurabh Datta ,  Dorin Comaniciu

IPC分类号： A61B8/06 ,  A61B5/00 ,  A61B8/08 ,  A61B8/00 ,  A61B8/14

CPC分类号： A61B8/065 ,  A61B5/1075 ,  A61B5/7264 ,  A61B8/085 ,  A61B8/14 ,  A61B8/463 ,  A61B8/466 ,  A61B8/483 ,  A61B8/486 ,  A61B8/488 ,  A61B8/5223

摘要： A mitral valve is detected in transthoracic echocardiography. The ultrasound transducer is positioned against the chest of the patient rather than being inserted within the patient. While data acquired from such scanning may be noisier or have less resolution, the mitral valve may still be automatically detected. Using both B-mode data representing tissue as well as flow data representing the regurgitant jet, the mitral valve may be detected automatically with a machine-learnt classifier. A series of classifiers may be used, such as determining a position and orientation of a valve region with one classifier, determining a regurgitant orifice with another classifier, and locating mitral valve anatomy with a third classifier. One or more features for some of the classifiers may be calculated based on the orientation of the valve region.

摘要翻译： 经胸超声心动图检测二尖瓣。 超声换能器位于患者胸部的位置，而不是插入患者体内。 虽然从这种扫描获取的数据可能噪声较大或分辨率较低，但仍可能自动检测二尖瓣。 使用表示组织的B模式数据以及表示反流喷射的流动数据，可以用机器学习的分类器自动检测二尖瓣。 可以使用一系列分类器，例如用一个分类器确定瓣膜区域的位置和取向，使用另一分类器确定反流孔，以及用第三分类器定位二尖瓣解剖。 可以基于阀区域的取向来计算一些分类器的一个或多个特征。

公开(公告)号：US09033887B2

公开(公告)日：2015-05-19

申请号：US13905690

申请日：2013-05-30

申请人： Razvan Ioan Ionasec ,  Dime Vitanovski ,  Yang Wang ,  Bogdan Georgescu ,  Ingmar Voigt ,  Saurabh Datta ,  Dorin Comaniciu

发明人： Razvan Ioan Ionasec ,  Dime Vitanovski ,  Yang Wang ,  Bogdan Georgescu ,  Ingmar Voigt ,  Saurabh Datta ,  Dorin Comaniciu

IPC分类号： A61B8/06 ,  A61B5/00 ,  A61B8/08 ,  A61B8/00 ,  A61B8/14 ,  A61B5/107

CPC分类号： A61B8/065 ,  A61B5/1075 ,  A61B5/7264 ,  A61B8/085 ,  A61B8/14 ,  A61B8/463 ,  A61B8/466 ,  A61B8/483 ,  A61B8/486 ,  A61B8/488 ,  A61B8/5223

摘要： A mitral valve is detected in transthoracic echocardiography. The ultrasound transducer is positioned against the chest of the patient rather than being inserted within the patient. While data acquired from such scanning may be noisier or have less resolution, the mitral valve may still be automatically detected. Using both B-mode data representing tissue as well as flow data representing the regurgitant jet, the mitral valve may be detected automatically with a machine-learnt classifier. A series of classifiers may be used, such as determining a position and orientation of a valve region with one classifier, determining a regurgitant orifice with another classifier, and locating mitral valve anatomy with a third classifier. One or more features for some of the classifiers may be calculated based on the orientation of the valve region.

摘要翻译： 经胸超声心动图检测二尖瓣。 超声换能器位于患者胸部的位置，而不是插入患者体内。 虽然从这种扫描获取的数据可能噪声较大或分辨率较低，但仍可能自动检测二尖瓣。 使用表示组织的B模式数据以及表示反流喷射的流动数据，可以用机器学习的分类器自动检测二尖瓣。 可以使用一系列分类器，例如用一个分类器确定瓣膜区域的位置和取向，使用另一分类器确定反流孔，以及用第三分类器定位二尖瓣解剖。 可以基于阀区域的取向来计算一些分类器的一个或多个特征。

公开(公告)号：US09135699B2

公开(公告)日：2015-09-15

申请号：US13826307

申请日：2013-03-14

申请人： Kristof Ralovich ,  Lucian Mihai Itu ,  Viorel Mihalef ,  Puneet Sharma ,  Razvan Ioan Ionasec ,  Dime Vitanovski ,  Waldemar Krawtschuk ,  Dorin Comaniciu

发明人： Kristof Ralovich ,  Lucian Mihai Itu ,  Viorel Mihalef ,  Puneet Sharma ,  Razvan Ioan Ionasec ,  Dime Vitanovski ,  Waldemar Krawtschuk ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  G06T7/00

CPC分类号： G06T7/0012 ,  G06T2207/10076 ,  G06T2207/10096 ,  G06T2207/30104

摘要： A method and system for non-invasive hemodynamic assessment of aortic coarctation from medical image data, such as magnetic resonance imaging (MRI) data is disclosed. Patient-specific lumen anatomy of the aorta and supra-aortic arteries is estimated from medical image data of a patient, such as contrast enhanced MRI. Patient-specific aortic blood flow rates are estimated from the medical image data of the patient, such as velocity encoded phase-contrasted MRI cine images. Patient-specific inlet and outlet boundary conditions for a computational model of aortic blood flow are calculated based on the patient-specific lumen anatomy, the patient-specific aortic blood flow rates, and non-invasive clinical measurements of the patient. Aortic blood flow and pressure are computed over the patient-specific lumen anatomy using the computational model of aortic blood flow and the patient-specific inlet and outlet boundary conditions.

摘要翻译： 公开了一种用于从医学图像数据（如磁共振成像（MRI））数据进行主动脉缩窄的非侵入性血流动力学评估的方法和系统。 从患者的医学图像数据估计主动脉和超主动脉血管的患者特异性内腔解剖结构，例如对比度增强的MRI。 根据患者的医学图像数据估计患者特异性主动脉血流速率，例如速度编码的相位对比MRI电影图像。 基于患者特异性内腔解剖，患者特异性主动脉血流速率和患者的非侵入性临床测量，计算主动脉血流计算模型的患者特异性入口和出口边界条件。 使用主动脉血流计算模型和患者特定的入口和出口边界条件，计算患者特异性内腔解剖结构的主动脉血流和压力。

公开(公告)号：US20130243294A1

公开(公告)日：2013-09-19

申请号：US13826307

申请日：2013-03-14

申请人： Kristof Ralovich ,  Lucian Mihai Itu ,  Viorel Mihalef ,  Puneet Sharma ,  Razvan Ioan Ionasec ,  Dime Vitanovski ,  Waldemar Krawtschuk ,  Dorin Comaniciu

发明人： Kristof Ralovich ,  Lucian Mihai Itu ,  Viorel Mihalef ,  Puneet Sharma ,  Razvan Ioan Ionasec ,  Dime Vitanovski ,  Waldemar Krawtschuk ,  Dorin Comaniciu

IPC分类号： G06T7/00

CPC分类号： G06T7/0012 ,  G06T2207/10076 ,  G06T2207/10096 ,  G06T2207/30104

摘要： A method and system for non-invasive hemodynamic assessment of aortic coarctation from medical image data, such as magnetic resonance imaging (MRI) data is disclosed. Patient-specific lumen anatomy of the aorta and supra-aortic arteries is estimated from medical image data of a patient, such as contrast enhanced MRI. Patient-specific aortic blood flow rates are estimated from the medical image data of the patient, such as velocity encoded phase-contrasted MRI cine images. Patient-specific inlet and outlet boundary conditions for a computational model of aortic blood flow are calculated based on the patient-specific lumen anatomy, the patient-specific aortic blood flow rates, and non-invasive clinical measurements of the patient. Aortic blood flow and pressure are computed over the patient-specific lumen anatomy using the computational model of aortic blood flow and the patient-specific inlet and outlet boundary conditions.

摘要翻译： 公开了一种用于从医学图像数据（如磁共振成像（MRI））数据进行主动脉缩窄的非侵入性血流动力学评估的方法和系统。 从患者的医学图像数据估计主动脉和超主动脉血管的患者特异性内腔解剖结构，例如对比度增强的MRI。 根据患者的医学图像数据估计患者特异性主动脉血流速率，例如速度编码的相位对比MRI电影图像。 基于患者特异性内腔解剖，患者特异性主动脉血流速率和患者的非侵入性临床测量，计算主动脉血流计算模型的患者特异性入口和出口边界条件。 使用主动脉血流计算模型和患者特定的入口和出口边界条件，计算患者特异性内腔解剖结构的主动脉血流和压力。

公开(公告)号：US08812431B2

公开(公告)日：2014-08-19

申请号：US13016259

申请日：2011-01-28

申请人： Ingmar Voigt ,  Dime Vitanovski ,  Razvan Ioan Ionasec ,  Alexey Tsymbal ,  Bogdan Georgescu ,  Shaohua Kevin Zhou ,  Martin Huber ,  Dorin Comaniciu

发明人： Ingmar Voigt ,  Dime Vitanovski ,  Razvan Ioan Ionasec ,  Alexey Tsymbal ,  Bogdan Georgescu ,  Shaohua Kevin Zhou ,  Martin Huber ,  Dorin Comaniciu

IPC分类号： G06F17/30 ,  G06F19/12 ,  G06F19/26 ,  A61B5/05 ,  G06N5/00 ,  G06G7/60

CPC分类号： G06G7/60 ,  G06N5/00

摘要： A method and system for providing medical decision support based on virtual organ models and learning based discriminative distance functions is disclosed. A patient-specific virtual organ model is generated from medical image data of a patient. One or more similar organ models to the patient-specific organ model are retrieved from a plurality of previously stored virtual organ models using a learned discriminative distance function. The patient-specific valve model can be classified into a first class or a second class based on the previously stored organ models determined to be similar to the patient-specific organ model.

摘要翻译： 公开了一种基于虚拟器官模型和基于学习的辨别距离函数提供医学决策支持的方法和系统。 从患者的医学图像数据生成患者特异性虚拟器官模型。 使用学习的鉴别距离函数从多个先前存储的虚拟器官模型检索与患者特异器官模型的一个或多个相似的器官模型。 基于先前存储的被确定为类似于患者特异器官模型的器官模型，患者特异性瓣膜模型可以分为第一类或第二类。

公开(公告)号：US20110191283A1

公开(公告)日：2011-08-04

申请号：US13016259

申请日：2011-01-28

申请人： Ingmar Voigt ,  Dime Vitanovski ,  Razvan Ioan Ionasec ,  Alexey Tsymbal ,  Bogdan Georgescu ,  Shaohua Kevin Zhou ,  Martin Huber ,  Dorin Comaniciu

发明人： Ingmar Voigt ,  Dime Vitanovski ,  Razvan Ioan Ionasec ,  Alexey Tsymbal ,  Bogdan Georgescu ,  Shaohua Kevin Zhou ,  Martin Huber ,  Dorin Comaniciu

IPC分类号： G06N5/00 ,  G06G7/60

CPC分类号： G06G7/60 ,  G06N5/00

摘要： A method and system for providing medical decision support based on virtual organ models and learning based discriminative distance functions is disclosed. A patient-specific virtual organ model is generated from medical image data of a patient. One or more similar organ models to the patient-specific organ model are retrieved from a plurality of previously stored virtual organ models using a learned discriminative distance function. The patient-specific valve model can be classified into a first class or a second class based on the previously stored organ models determined to be similar to the patient-specific organ model.

摘要翻译： 公开了一种基于虚拟器官模型和基于学习的辨别距离函数提供医学决策支持的方法和系统。 从患者的医学图像数据生成患者特异性虚拟器官模型。 使用学习的鉴别距离函数从多个先前存储的虚拟器官模型检索与患者特异器官模型的一个或多个相似的器官模型。 基于先前存储的被确定为类似于患者特异器官模型的器官模型，患者特异性瓣膜模型可以分为第一类或第二类。