公开(公告)号：US20060064007A1

公开(公告)日：2006-03-23

申请号：US11218018

申请日：2005-09-01

申请人： Dorin Comaniciu ,  Bogdan Georgescu ,  Jing Xiao ,  Xiang Zhou

发明人： Dorin Comaniciu ,  Bogdan Georgescu ,  Jing Xiao ,  Xiang Zhou

IPC分类号： A61B5/05

CPC分类号： G06K9/3216 ,  A61B8/08 ,  A61B8/0883 ,  A61B8/463 ,  A61B8/483 ,  G06K2209/051 ,  G06T7/0016 ,  G06T7/12 ,  G06T7/149 ,  G06T7/246 ,  G06T7/277 ,  G06T2200/04 ,  G06T2200/24 ,  G06T2207/10136 ,  G06T2207/30048 ,  Y10S128/922

摘要： A system and method for defining and tracking a deformable shape of a candidate anatomical structure wall in a three dimensional (3D) image is disclosed. The shape of the candidate anatomical structure is represented by a plurality of labeled 3D landmark points. At least one 3D landmark point of the deformable shape in an image frame is defined. A 3D cuboid is defined around the detected 3D landmark point. For each landmark point associated with the anatomical structure, its location and location uncertainty matrix is estimated in subsequent frames relative to the reference anatomical structures. A shape model is generated to represent dynamics of the deformable shape in subsequent image frames. The shape model includes statistical information from a training data set of 3D images of representative anatomical structures. The shape model is aligned to the deformable shape of the candidate anatomical structure. The shape model is fused with the deformable shape. A current shape of the candidate anatomical structure is estimated.

公开(公告)号：US20050147303A1

公开(公告)日：2005-07-07

申请号：US10991933

申请日：2004-11-18

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

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

IPC分类号： G06K9/62 ,  G06K9/64 ,  G06T7/00 ,  G06K9/00 ,  G06K9/46

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：US10719986B2

公开(公告)日：2020-07-21

申请号：US12975714

申请日：2010-12-22

申请人： Dominik Zaeuner ,  Razvan Ioan Ionasec ,  Bogdan Georgescu ,  Yefeng Zheng ,  Dorin Comaniciu ,  Ingmar Voigt ,  Jan Boese

发明人： Dominik Zaeuner ,  Razvan Ioan Ionasec ,  Bogdan Georgescu ,  Yefeng Zheng ,  Dorin Comaniciu ,  Ingmar Voigt ,  Jan Boese

IPC分类号： G06T17/00 ,  G06T19/00 ,  G16H50/50 ,  G06F19/00

摘要： A method and system for virtual percutaneous valve implantation is disclosed. A patient-specific anatomical model of a heart valve is estimated based on 3D cardiac medical image data and an implant model representing a valve implant is virtually deployed into the patient-specific anatomical model of the heart valve. A library of implant models, each modeling geometrical properties of a corresponding valve implant, is maintained. The implant models maintained in the library are virtually deployed into the patient specific anatomical model of the heart valve to select an implant type and size and deployment location and orientation for percutaneous valve implantation.

公开(公告)号：US10311978B2

公开(公告)日：2019-06-04

申请号：US13754174

申请日：2013-01-30

申请人： Tommaso Mansi ,  Bogdan Georgescu ,  Xudong Zheng ,  Ali Kamen ,  Dorin Comaniciu

发明人： Tommaso Mansi ,  Bogdan Georgescu ,  Xudong Zheng ,  Ali Kamen ,  Dorin Comaniciu

IPC分类号： G06F17/50 ,  A61B5/055 ,  A61N1/362 ,  A61N1/365 ,  G06F19/00 ,  G06T17/00 ,  G09B23/30 ,  G16H50/50 ,  A61B5/00 ,  A61B8/08

CPC分类号： G06F17/5009 ,  A61B5/0044 ,  A61B5/055 ,  A61B8/0883 ,  A61B2576/023 ,  A61N1/3627 ,  A61N1/36514 ,  A61N1/36585 ,  G06T17/00 ,  G06T2210/41 ,  G09B23/30 ,  G16H50/50

摘要： A method and system for patient-specific planning of cardiac therapy, such as cardiac resynchronization therapy (CRT), based on preoperative clinical data and medical images, such as ECG data, magnetic resonance imaging (MRI) data, and ultrasound data, is disclosed. A patient-specific anatomical model of the left and right ventricles is generated from medical image data of a patient. A patient-specific computational heart model, which comprises cardiac electrophysiology, biomechanics and hemodynamics, is generated based on the patient-specific anatomical model of the left and right ventricles and clinical data. Simulations of cardiac therapies, such as CRT at one or more anatomical locations are performed using the patient-specific computational heart model. Changes in clinical cardiac parameters are then computed from the patient-specific model, constituting predictors of therapy outcome useful for therapy planning and optimization.

公开(公告)号：US09761004B2

公开(公告)日：2017-09-12

申请号：US12487223

申请日：2009-06-18

申请人： Sushil Mittal ,  Yefeng Zheng ,  Bogdan Georgescu ,  Fernando Vega-Higuera ,  Dorin Comaniciu

发明人： Sushil Mittal ,  Yefeng Zheng ,  Bogdan Georgescu ,  Fernando Vega-Higuera ,  Dorin Comaniciu

IPC分类号： A61B6/03 ,  G06T7/00 ,  G06T7/13

CPC分类号： G06T7/13 ,  G06T7/0012 ,  G06T2207/10081 ,  G06T2207/20044 ,  G06T2207/20081 ,  G06T2207/30048 ,  G06T2207/30101

摘要： A method and system for automatic coronary stenosis detection in computed tomography (CT) data is disclosed. Coronary artery centerlines are obtained in an input cardiac CT volume. A trained classifier, such as a probabilistic boosting tree (PBT) classifier, is used to detect stenosis regions along the centerlines in the input cardiac CT volume. The classifier classifies each of the control points that define the coronary artery centerlines as a stenosis point or a non-stenosis point.

公开(公告)号：US09375195B2

公开(公告)日：2016-06-28

申请号：US13905939

申请日：2013-05-30

申请人： Ali Kamen ,  Tommaso Mansi ,  Bogdan Georgescu ,  Dorin Comaniciu

发明人： Ali Kamen ,  Tommaso Mansi ,  Bogdan Georgescu ,  Dorin Comaniciu

IPC分类号： G06K9/00 ,  A61B8/08 ,  A61B8/13 ,  A61B10/02 ,  A61B5/00 ,  G06T7/00 ,  A61B5/05 ,  A61B5/055 ,  A61B8/14 ,  A61B8/00

CPC分类号： A61B8/0841 ,  A61B5/055 ,  A61B5/7425 ,  A61B8/13 ,  A61B8/14 ,  A61B8/4254 ,  A61B8/463 ,  A61B8/483 ,  A61B8/485 ,  A61B8/5261 ,  A61B10/0233 ,  A61B10/0241 ,  G06T7/33 ,  G06T2207/10136 ,  G06T2207/30081

摘要： A method and system for real-time ultrasound guided prostate needle biopsy based on a biomechanical model of the prostate from 3D planning image data, such as magnetic resonance imaging (MRI) data, is disclosed. The prostate is segmented in the 3D ultrasound image. A reference patient-specific biomechanical model of the prostate extracted from planning image data is fused to a boundary of the segmented prostate in the 3D ultrasound image, resulting in a fused 3D biomechanical prostate model. In response to movement of an ultrasound probe to a new location, a current 2D ultrasound image is received. The fused 3D biomechanical prostate model is deformed based on the current 2D ultrasound image to match a current deformation of the prostate due to the movement of the ultrasound probe to the new location.

摘要翻译： 公开了一种基于3D规划图像数据（如磁共振成像（MRI））数据的前列腺生物力学模型的实时超声引导前列腺穿刺活检的方法和系统。 前列腺在3D超声图像中被分割。 从计划图像数据中提取的前列腺特异性参考生物力学模型融合到3D超声图像中分割的前列腺的边界，产生融合的3D生物力学前列腺模型。 响应于超声探头移动到新位置，接收当前的2D超声图像。 融合的3D生物力学前列腺模型基于当前的2D超声图像而变形，以匹配由于超声探头移动到新位置而导致的前列腺的当前变形。

公开(公告)号：US09119540B2

公开(公告)日：2015-09-01

申请号：US13226779

申请日：2011-09-07

申请人： Puneet Sharma ,  Bogdan Georgescu ,  Viorel Mihalef ,  Terrence Chen ,  Dorin Comaniciu

发明人： Puneet Sharma ,  Bogdan Georgescu ,  Viorel Mihalef ,  Terrence Chen ,  Dorin Comaniciu

IPC分类号： A61B5/026 ,  G06T7/00 ,  G06F19/00

CPC分类号： A61B5/026 ,  A61B2576/023 ,  G06F19/321 ,  G06F19/3468 ,  G06T7/0016 ,  G06T2207/10081 ,  G06T2207/10096 ,  G06T2207/10116 ,  G06T2207/30048 ,  G06T2207/30104 ,  G16H20/17 ,  G16H30/20 ,  G16H30/40 ,  G16H50/50

摘要： In order to assess coronary artery disease from medical image data, an anatomical model of a coronary artery is generated from the medical image data. A velocity of blood in the coronary artery is estimated based on a spatio-temporal representation of contrast agent propagation in the medical image data. Blood flow is simulated in the anatomical model of the coronary artery using a computational fluid dynamics (CFD) simulation using the estimated velocity of the blood in the coronary artery as a boundary condition.

摘要翻译： 为了从医学图像数据中评估冠状动脉疾病，从医学图像数据生成冠状动脉的解剖模型。 基于医学图像数据中的造影剂传播的时空表示来估计冠状动脉中的血液速度。 使用计算流体动力学（CFD）模拟，使用冠状动脉中血液的估计速度作为边界条件，在冠状动脉的解剖模型中模拟血流。

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：US20140058715A1

公开(公告)日：2014-02-27

申请号：US14070810

申请日：2013-11-04

申请人： Puneet Sharma ,  Lucian Mihai Itu ,  Ali Kamen ,  Bogdan Georgescu ,  Xudong Zheng ,  Huseyin Tek ,  Dorin Comaniciu ,  Dominik Bernhardt ,  Fernando Vega-Higuera ,  Michael Scheuering

发明人： Puneet Sharma ,  Lucian Mihai Itu ,  Ali Kamen ,  Bogdan Georgescu ,  Xudong Zheng ,  Huseyin Tek ,  Dorin Comaniciu ,  Dominik Bernhardt ,  Fernando Vega-Higuera ,  Michael Scheuering

IPC分类号： G06F19/00

CPC分类号： G16B5/00 ,  A61B5/02007 ,  A61B5/1128 ,  A61B6/032 ,  A61B6/503 ,  A61B6/5217 ,  A61B2576/023 ,  G16H50/50

摘要： A method and system for non-invasive assessment of coronary artery stenosis is disclosed. Patient-specific anatomical measurements of the coronary arteries are extracted from medical image data of a patient acquired during rest state. Patient-specific rest state boundary conditions of a model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements and non-invasive clinical measurements of the patient at rest. Patient-specific rest state boundary conditions of the model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements and non-invasive clinical measurements of the patient at rest. Hyperemic blood flow and pressure across at least one stenosis region of the coronary arteries are simulated using the model of coronary circulation and the patient-specific hyperemic boundary conditions. Fractional flow reserve (FFR) is calculated for the at least one stenosis region based on the simulated hyperemic blood flow and pressure.

摘要翻译： 公开了冠状动脉狭窄非侵入性评估的方法和系统。 从休息状态下获得的患者的医学图像数据中提取冠状动脉的患者特异性解剖学测量。 基于患者特异性解剖学测量和静息患者的非侵入性临床测量，计算代表冠状动脉的冠状动脉循环模型的患者特异性休息状态边界条件。 基于患者特异性解剖学测量和静息患者的非侵入性临床测量，计算代表冠状动脉的冠状动脉循环模型的患者特异性休息状态边界条件。 使用冠状动脉循环模型和患者特异性充血边界条件模拟冠状动脉至少一个狭窄区域的血流量和压力。 基于模拟的充血血流量和压力计算至少一个狭窄区域的分数流量储备（FFR）。

公开(公告)号：US20130246034A1

公开(公告)日：2013-09-19

申请号：US13794113

申请日：2013-03-11

申请人： Puneet Sharma ,  Lucian Mihai Itu ,  Ali Kamen ,  Bogdan Georgescu ,  Xudong Zheng ,  Huseyin Tek ,  Dorin Comaniciu ,  Dominik Bernhardt ,  Fernando Vega-Higuera ,  Michael Scheuering

发明人： Puneet Sharma ,  Lucian Mihai Itu ,  Ali Kamen ,  Bogdan Georgescu ,  Xudong Zheng ,  Huseyin Tek ,  Dorin Comaniciu ,  Dominik Bernhardt ,  Fernando Vega-Higuera ,  Michael Scheuering

IPC分类号： G06F19/12

CPC分类号： G16B5/00 ,  A61B5/02007 ,  A61B5/1128 ,  A61B6/032 ,  A61B6/503 ,  A61B6/5217 ,  A61B2576/023 ,  G16H50/50

摘要： A method and system for non-invasive assessment of coronary artery stenosis is disclosed. Patient-specific anatomical measurements of the coronary arteries are extracted from medical image data of a patient acquired during rest state. Patient-specific rest state boundary conditions of a model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements and non-invasive clinical measurements of the patient at rest. Patient-specific rest state boundary conditions of the model of coronary circulation representing the coronary arteries are calculated based on the patient-specific anatomical measurements and non-invasive clinical measurements of the patient at rest. Hyperemic blood flow and pressure across at least one stenosis region of the coronary arteries are simulated using the model of coronary circulation and the patient-specific hyperemic boundary conditions. Fractional flow reserve (FFR) is calculated for the at least one stenosis region based on the simulated hyperemic blood flow and pressure.

摘要翻译： 公开了冠状动脉狭窄非侵入性评估的方法和系统。 从休息状态下获得的患者的医学图像数据中提取冠状动脉的患者特异性解剖学测量。 基于患者特异性解剖学测量和静息患者的非侵入性临床测量，计算代表冠状动脉的冠状动脉循环模型的患者特异性休息状态边界条件。 基于患者特异性解剖学测量和静息患者的非侵入性临床测量，计算代表冠状动脉的冠状动脉循环模型的患者特异性休息状态边界条件。 使用冠状动脉循环模型和患者特异性充血边界条件模拟冠状动脉至少一个狭窄区域的血流量和压力。 基于模拟的充血血流量和压力计算至少一个狭窄区域的分数流量储备（FFR）。