公开(公告)号：US20130129173A1

公开(公告)日：2013-05-23

申请号：US13682820

申请日：2012-11-21

申请人： Sasa Grbic ,  Razvan Ioan Ionasec ,  Fernando Vega-Higuera ,  Dominik Bernhardt ,  Bogdan Georgescu ,  Dorin Comaniciu

发明人： Sasa Grbic ,  Razvan Ioan Ionasec ,  Fernando Vega-Higuera ,  Dominik Bernhardt ,  Bogdan Georgescu ,  Dorin Comaniciu

IPC分类号： G06T7/00

CPC分类号： G06T7/0012 ,  G06T7/75 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method and system for automated intervention planning for transcatheter aortic valve implantations using computed tomography (CT) data is disclosed. A patient-specific aortic valve model is detected in a CT volume of a patient. The patient-specific aortic valve model is detected by detecting a global location of the patient-specific aortic valve model in the CT volume, detecting aortic valve landmarks based on the detected global location, and fitting an aortic root surface model. Angulation parameters of a C-arm imaging device for acquiring intra-operative fluoroscopic images and anatomical measurements of the aortic valve are automatically determined based on the patient-specific aortic valve model.

摘要翻译： 公开了一种使用计算机断层扫描（CT）数据进行经导管主动脉瓣植入术的自动干预计划的方法和系统。 在患者的CT体积中检测患者特异性主动脉瓣模型。 通过检测CT体积中患者特异性主动脉瓣模型的全局位置检测患者特异性主动脉瓣模型，根据检测到的全局位置检测主动脉瓣标志，并配合主动脉根部表面模型。 基于患者特异性主动脉瓣模型自动确定用于获取手术中透视图像和主动脉瓣的解剖学测量的C形臂成像装置的角度参数。

公开(公告)号：US08406496B2

公开(公告)日：2013-03-26

申请号：US12504047

申请日：2009-07-16

申请人： Yefeng Zheng ,  Xiaoguang Lu ,  Bogdan Georgescu ,  Edgar Müller ,  Dorin Comaniciu ,  Arne Littmann

发明人： Yefeng Zheng ,  Xiaoguang Lu ,  Bogdan Georgescu ,  Edgar Müller ,  Dorin Comaniciu ,  Arne Littmann

IPC分类号： G06K9/00

CPC分类号： G06K9/6202 ,  G06K2209/051

摘要： A method and system for left ventricle (LV) detection in 2D magnetic resonance imaging (MRI) images is disclosed. In order to detect the LV in a 2D MRI image, a plurality of LV candidates are detected, for example using marginal space learning (MSL) based detection. Candidates for distinctive anatomic landmarks associated with the LV are then detected in the 2D MRI image. In particular, apex candidates and base candidates are detected in the 2D MRI image. One of the LV candidates is selected as a final LV detection result using component-based voting based on the detected LV candidates, apex candidates, and base candidates.

摘要翻译： 公开了一种用于2D磁共振成像（MRI）图像中左心室（LV）检测的方法和系统。 为了检测2D MRI图像中的LV，例如使用基于边缘空间学习（MSL）的检测来检测多个LV候选。 然后在2D MRI图像中检测与LV相关的特征性解剖学标记的候选者。 特别地，在2D MRI图像中检测到顶点候选和基础候选。 使用基于检测到的LV候选，顶点候选和基础候选的基于组件的投票，将LV候选中的一个选择为最终的LV检测结果。

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：US08009887B2

公开(公告)日：2011-08-30

申请号：US12288215

申请日：2008-10-17

申请人： Razvan Ionasec ,  Bogdan Georgescu ,  Helene C. Houle ,  Sebastian Vogt ,  Michael Scheuering ,  Estelle Camus ,  Dorin Comaniciu

发明人： Razvan Ionasec ,  Bogdan Georgescu ,  Helene C. Houle ,  Sebastian Vogt ,  Michael Scheuering ,  Estelle Camus ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/00 ,  G06K2209/051 ,  G06T7/0016 ,  G06T7/251 ,  G06T17/20 ,  G06T2207/10076 ,  G06T2207/10136 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/30048

摘要： A method and system for modeling the aortic valve in 4D image data, such as 4D CT and echocardiography, is disclosed. An initial estimate of a physiological aortic valve model is determined for at least one reference frame of a 4D image sequence based on anatomic features in the reference frame. The initial estimate is refined to generate a final estimate in the reference frame. A dynamic model of the aortic valve is then generated by estimating the physiological aortic valve model for each remaining frame of the 4D image sequence based on the final estimate in the reference frame. The aortic valve can be quantitatively evaluated using the dynamic model.

摘要翻译： 公开了用于4D图像数据建模主动脉瓣的方法和系统，如4D CT和超声心动图。 基于参考系中的解剖特征，为4D图像序列的至少一个参考帧确定生理主动脉瓣模型的初始估计。 改进初始估计以在参考系中产生最终估计。 然后通过基于参考框架中的最终估计估计4D图像序列的每个剩余帧的生理主动脉瓣模型来生成主动脉瓣的动态模型。 主动脉瓣可以使用动态模型进行定量评估。

公开(公告)号：US07965869B2

公开(公告)日：2011-06-21

申请号：US12401909

申请日：2009-03-11

申请人： Xiang Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu ,  R. Bharat Rao ,  Alok Gupta

发明人： Xiang Zhou ,  Bogdan Georgescu ,  Dorin Comaniciu ,  R. Bharat Rao ,  Alok Gupta

IPC分类号： G06K9/00 ,  G06K9/46 ,  G06K9/36

CPC分类号： G06K9/6206 ,  G06K9/6256 ,  G06T7/12 ,  G06T7/149 ,  G06T7/251 ,  G06T2207/10132 ,  G06T2207/30048

摘要： A detection framework that matches anatomical structures using appearance and shape is disclosed. A training set of images are used in which object shapes or structures are annotated in the images. A second training set of images represents negative examples for such shapes and structures, i.e., images containing no such objects or structures. A classification algorithm trained on the training sets is used to detect a structure at its location. The structure is matched to a counterpart in the training set that can provide details about the structure's shape and appearance.

摘要翻译： 公开了一种使用外观和形状匹配解剖结构的检测框架。 使用图像的训练集，其中在图像中注释对象形状或结构。 图像的第二训练集表示这种形状和结构的负面例子，即不包含这样的对象或结构的图像。 训练集训练的分类算法用于检测其位置的结构。 该结构与训练集中的对应物匹配，可以提供关于结构形状和外观的细节。

公开(公告)号：US20100239148A1

公开(公告)日：2010-09-23

申请号：US12725679

申请日：2010-03-17

申请人： Yefeng Zheng ,  Bogdan Georgescu ,  Matthias John ,  Jan Boese ,  Dorin Comaniciu

发明人： Yefeng Zheng ,  Bogdan Georgescu ,  Matthias John ,  Jan Boese ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： G06K9/4638 ,  G06K2209/051 ,  G06T7/12 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/30101

摘要： A method and system for aorta segmentation in a 3D volume, such as a C-arm CT volume is disclosed. The aortic root is detected in the 3D volume using marginal space learning (MSL) based segmentation. The aortic arch is detected in the 3D volume using MSL based segmentation. The ascending aorta is tracked from the aortic root to the aortic arch in the 3D volume, and the descending aorta is tracked from the aortic arch in the 3D volume.

摘要翻译： 公开了一种用于3D体积中的主动脉分割的方法和系统，例如C臂CT体积。 使用基于边缘空间学习（MSL）的分割在3D体积中检测主动脉根部。 使用基于MSL的分割在3D体积中检测主动脉弓。 升主动脉从主动脉根部追溯到3D体积中的主动脉弓，并且主动脉弓从3D体积中跟踪降主动脉。

公开(公告)号：US07792342B2

公开(公告)日：2010-09-07

申请号：US11675678

申请日：2007-02-16

申请人： Adrian Barbu ,  Vassilis Athitsos ,  Bogdan Georgescu ,  Peter Durlak ,  Stefan Boehm ,  Dorin Comaniciu

发明人： Adrian Barbu ,  Vassilis Athitsos ,  Bogdan Georgescu ,  Peter Durlak ,  Stefan Boehm ,  Dorin Comaniciu

IPC分类号： G06K9/00

CPC分类号： A61B6/12 ,  G06K9/48 ,  G06K2209/057

摘要： A system and method for populating a database with a set of image sequences of an object is disclosed. The database is used to detect localization of a guidewire in the object. A set of images of anatomical structures is received in which each image is annotated to show a guidewire, catheter, wire tip and stent. For each given image a Probabilistic Boosting Tree (PBT) is used to detect short line segments of constant length in the image. Two segment curves are constructed from the short line segments. A discriminative joint shape and appearance model is used to classify each two segment curve. A shape of an n-segment curve is constructed by concatenating all the two segment curves. A guidewire curve model is identified that includes a start point, end point and the n-segment curve. The guidewire curve model is stored in the database.

摘要翻译： 公开了一种使用一组对象的图像序列填充数据库的系统和方法。 数据库用于检测对象中导丝的定位。 接收一组解剖结构的图像，其中每个图像被注释以示出导丝，导管，线尖和支架。 对于每个给定的图像，使用概率增强树（PBT）来检测图像中恒定长度的短线段。 从短线段构建两段曲线。 使用歧视关节形状和外观模型对每个两段曲线进行分类。 通过连接所有两个段曲线构建n段曲线的形状。 识别出导线曲线模型，其包括起始点，终点和n段曲线。 导丝曲线模型存储在数据库中。

公开(公告)号：US20100142787A1

公开(公告)日：2010-06-10

申请号：US12630061

申请日：2009-12-03

申请人： Yefeng Zheng ,  Bogdan Georgescu ,  Arne Littmann ,  Edgar Mueller ,  Dorin Comaniciu

发明人： Yefeng Zheng ,  Bogdan Georgescu ,  Arne Littmann ,  Edgar Mueller ,  Dorin Comaniciu

IPC分类号： G06K9/62

CPC分类号： G06K9/3241 ,  G06K2209/051 ,  G06T7/11 ,  G06T7/143 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/30048

摘要： A method and system for left ventricle (LV) detection in 2D magnetic resonance imaging (MRI) images is disclosed. In order to detect the LV in a 2D MRI image, a plurality of LV candidates are detected, for example using marginal space learning (MSL) based detection. Candidates for distinctive anatomic landmarks associated with the LV are then detected in the 2D MRI image. In particular, apex candidates and base candidates are detected in the 2D MRI image. One of the LV candidates is selected as a final LV detection result by ranking the LV candidates based on the LV candidates, the apex candidates, and the base candidates using a trained ranking model.

摘要翻译： 公开了一种用于2D磁共振成像（MRI）图像中左心室（LV）检测的方法和系统。 为了检测2D MRI图像中的LV，例如使用基于边缘空间学习（MSL）的检测来检测多个LV候选。 然后在2D MRI图像中检测与LV相关的特征性解剖学标记的候选者。 特别地，在2D MRI图像中检测到顶点候选和基础候选。 通过使用训练排名模型，基于LV候选，顶点候选和基础候选对LV候选进行排名，将LV候选者中的一个选择为最终的LV检测结果。

公开(公告)号：US20090093717A1

公开(公告)日：2009-04-09

申请号：US12239898

申请日：2008-09-29

申请人： Gustavo Henrique Monteiro de Barros Carneiro ,  Fernando Amat ,  Bogdan Georgescu ,  Sara Good ,  Dorin Comaniciu

发明人： Gustavo Henrique Monteiro de Barros Carneiro ,  Fernando Amat ,  Bogdan Georgescu ,  Sara Good ,  Dorin Comaniciu

IPC分类号： A61B5/107 ,  A61B8/00

CPC分类号： A61B8/13 ,  A61B5/1075 ,  A61B5/7267 ,  A61B8/0816 ,  A61B8/0866 ,  A61B8/466 ,  G06F19/00 ,  G06F19/321 ,  G06K9/72 ,  G06K2209/05 ,  G06T7/11 ,  G06T2207/10136 ,  G06T2207/30044 ,  G16H50/20

摘要： A fetal parameter or anatomy is measured or detected from three-dimensional ultrasound data. An algorithm is machine-trained to detect fetal anatomy. Any machine training approach may be used. The machine-trained classifier is a joint classifier, such that one anatomy is detected using the ultrasound data and the detected location of another anatomy. The machine-trained classifier uses marginal space such that the location of anatomy is detected sequentially through translation, orientation and scale rather than detecting for all location parameters at once. The machine-trained classifier includes detectors for detecting from the ultrasound data at different resolutions, such as in a pyramid volume.

摘要翻译： 从三维超声数据测量或检测胎儿参数或解剖结构。 机器训练的算法用于检测胎儿解剖。 可以使用任何机器训练方法。 机器训练的分类器是联合分类器，使得使用超声数据和检测到的另一解剖结构的位置来检测一个解剖结构。 机器训练的分类器使用边缘空间，使得通过平移，取向和尺度顺序地检测解剖结构的位置，而不是一次检测所有位置参数。 机器训练分类器包括用于以不同分辨率例如金字塔体积的超声数据进行检测的检测器。

公开(公告)号：US20080101676A1

公开(公告)日：2008-05-01

申请号：US11856878

申请日：2007-09-18

申请人： Yefeng Zheng ,  Adrian Barbu ,  Bogdan Georgescu ,  Michael Sheuering ,  Dorin Comaniciu

发明人： Yefeng Zheng ,  Adrian Barbu ,  Bogdan Georgescu ,  Michael Sheuering ,  Dorin Comaniciu

IPC分类号： A61B5/00

CPC分类号： A61B6/503 ,  A61B8/0883 ,  G06K9/6247 ,  G06T7/12 ,  G06T7/149 ,  G06T2207/10081 ,  G06T2207/30048

摘要： A system and method for segmenting chambers of a heart in three dimensional images is disclosed. A set of three dimensional images of a heart is received. The shape of the heart in the three dimensional images is localized. Boundaries of the chambers of the heart in the localized shape are identified using steerable features.

摘要翻译： 公开了一种用于在三维图像中分割心脏腔室的系统和方法。 接收一组心脏的三维图像。 三维图像中心脏的形状是局部化的。 使用可转向特征来识别局部形状的心脏腔室的边界。