公开(公告)号：US20130246034A1

公开(公告)日：2013-09-19

申请号：US13794113

申请日：2013-03-11

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

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

IPC: G06F19/12

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

Abstract: 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.

Abstract translation: 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：US08527251B2

公开(公告)日：2013-09-03

申请号：US12770850

申请日：2010-04-30

Applicant: Razvan Ioan Ionasec ,  Puneet Sharma ,  Bogdan Georgescu ,  Andrey Torzhkov ,  Fabian Moerchen ,  Gayle M. Wittenberg ,  Dmitriy Fradkin ,  Dorin Comaniciu

Inventor： Razvan Ioan Ionasec ,  Puneet Sharma ,  Bogdan Georgescu ,  Andrey Torzhkov ,  Fabian Moerchen ,  Gayle M. Wittenberg ,  Dmitriy Fradkin ,  Dorin Comaniciu

IPC: G06G7/58

CPC classification number: A61B5/0263 ,  G01R33/56 ,  G01R33/5608 ,  G06F19/00 ,  G06F19/321 ,  G06T17/00 ,  G16H50/20 ,  G16H50/50

Abstract: A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A multi-component patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. Fluid Structure Interaction (FSI) simulations are performed on the patient specific anatomical model, and patient specific clinical parameters are extracted based on the patient specific heart model and the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.

Abstract translation: 公开了一种用于生成患者特异性解剖心脏模型的方法和系统。 接收患者心脏区域的体积图像数据，例如计算机断层摄影（CT），超声心动图或磁共振（MR）图像数据。 用于多个心脏组件的单独模型，例如左心室（LV）心内膜，LV心外膜，右心室（RV），左心房（LA），右心房（RA），二尖瓣，主动脉瓣，主动脉和肺动脉干， 在所述体积心脏图像数据中估计。 通过对每个心脏组件的各个模型进行整合，产生多组分患者特异性解剖心脏模型。 对患者特异性解剖模型进行流体结构相互作用（FSI）模拟，并根据患者特异性心脏模型和FSI模拟提取患者特异性临床参数。 疾病进展模型和风险分层是根据患者的具体临床参数进行的。

公开(公告)号：US08526699B2

公开(公告)日：2013-09-03

申请号：US13040716

申请日：2011-03-04

Applicant: Sushil Mittal ,  Yefeng Zheng ,  Bogdan Georgescu ,  Fernando Vega-Higuera ,  Shaohua Kevin Zhou ,  Dorin Comaniciu ,  Michael Kelm ,  Alexey Tsymbal ,  Dominik Bernhardt

Inventor： Sushil Mittal ,  Yefeng Zheng ,  Bogdan Georgescu ,  Fernando Vega-Higuera ,  Shaohua Kevin Zhou ,  Dorin Comaniciu ,  Michael Kelm ,  Alexey Tsymbal ,  Dominik Bernhardt

IPC: G06K9/00 ,  A61B5/05 ,  A61B5/02 ,  A61F2/06

CPC classification number: G06T7/0016 ,  G06T7/143 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/30101 ,  G06T2207/30172

Abstract: A method and system for providing detecting and classifying coronary stenoses in 3D CT image data is disclosed. Centerlines of coronary vessels are extracted from the CT image data. Non-vessel regions are detected and removed from the coronary vessel centerlines. The cross-section area of the lumen is estimated based on the coronary vessel centerlines using a trained regression function. Stenosis candidates are detected in the coronary vessels based on the estimated lumen cross-section area, and the significant stenosis candidates are automatically classified as calcified, non-calcified, or mixed.

Abstract translation: 公开了一种在3D CT图像数据中提供冠状动脉狭窄检测和分类的方法和系统。 从CT图像数据中提取冠脉血管的中心线。 从血管中心线检测和移除非血管区域。 使用经过训练的回归函数，基于冠状血管中心线估计管腔的横截面面积。 基于估计的管腔横截面积在冠状血管中检测狭窄候选物，并且显着狭窄候选物被自动分类为钙化，非钙化或混合。

公开(公告)号：US08423121B2

公开(公告)日：2013-04-16

申请号：US12538456

申请日：2009-08-10

Applicant: Peng Wang ,  Ying Zhu ,  Wei Zhang ,  Terrence Chen ,  Peter Durlak ,  Ulrich Bill ,  Dorin Comaniciu

Inventor： Peng Wang ,  Ying Zhu ,  Wei Zhang ,  Terrence Chen ,  Peter Durlak ,  Ulrich Bill ,  Dorin Comaniciu

IPC: A61B5/05

CPC classification number: A61B6/12 ,  A61B2034/2065 ,  A61B2090/376 ,  G06T7/246 ,  G06T2207/10016 ,  G06T2207/10121 ,  G06T2207/30021

Abstract: A method and system for tracking a guidewire in a fluoroscopic image sequence is disclosed. In order to track a guidewire in a fluoroscopic image sequence, guidewire segments are detected in each frame of the fluoroscopic image sequence. The guidewire in each frame of the fluoroscopic image sequence is then detected by rigidly tracking the guidewire from a previous frame of the fluoroscopic image sequence based on the detected guidewire segments in the current frame. The guidewire is then non-rigidly deformed in each frame based on the guidewire position in the previous frame.

Abstract translation: 公开了一种用于跟踪透视图像序列中的导丝的方法和系统。 为了在透视图像序列中跟踪导丝，在荧光镜图像序列的每个帧中检测导丝段。 然后通过基于当前帧中检测到的导丝段从透视图像序列的前一帧刚性跟踪导丝来检测透视图像序列的每个帧中的导丝。 然后基于先前框架中的导丝线位置，导丝在每个框架中不会刚性变形。

公开(公告)号：US20130077841A1

公开(公告)日：2013-03-28

申请号：US13602730

申请日：2012-09-04

Applicant: Dijia Wu ,  David Liu ,  Christian Tietjen ,  Grzegorz Soza ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

Inventor： Dijia Wu ,  David Liu ,  Christian Tietjen ,  Grzegorz Soza ,  Shaohua Kevin Zhou ,  Dorin Comaniciu

IPC: G06K9/46

CPC classification number: G06K9/6207 ,  G06T7/12 ,  G06T7/149 ,  G06T2207/10072 ,  G06T2207/20081 ,  G06T2207/20116 ,  G06T2207/30008 ,  G06T2207/30172

Abstract: A method and system for extracting rib centerlines in a 3D volume, such as a 3D computed tomography (CT) volume, is disclosed. Rib centerline voxels are detected in the 3D volume using a learning based detector. Rib centerlines or the whole rib cage are then extracted by matching a template of rib centerlines for the whole rib cage to the 3D volume based on the detected rib centerline voxels. Each of the extracted rib centerlines are then individually refined using an active contour model.

Abstract translation: 公开了一种用于在3D体积中提取肋骨中心线的方法和系统，例如3D计算机断层摄影（CT）体积。 使用基于学习的检测器在3D体积中检测肋中心线体素。 然后通过将基于检测到的肋骨中心线体素的整个肋骨架的肋骨中心线的模板与3D体积匹配来提取肋中心线或整个肋骨笼。 然后使用主动轮廓模型分别提取每个提取的肋中心线。

公开(公告)号：US20130072782A1

公开(公告)日：2013-03-21

申请号：US13616548

申请日：2012-09-14

Applicant: David Liu ,  Shaohua Kevin Zhou ,  Peter Gall ,  Dorin Comaniciu ,  Andre de Aliveira ,  Berthold Kiefer

Inventor： David Liu ,  Shaohua Kevin Zhou ,  Peter Gall ,  Dorin Comaniciu ,  Andre de Aliveira ,  Berthold Kiefer

IPC: A61B5/055

CPC classification number: G06T7/0034 ,  A61B5/055 ,  G01R33/5608 ,  G01R33/565 ,  G06T7/35 ,  G06T2207/10088 ,  G06T2207/30004

Abstract: A method and system for automatic magnetic resonance (MR) volume composition and normalization is disclosed. In one embodiment, a plurality of MR volumes is received. A composite MR volume is generated from the plurality of MR volumes. Volume normalization of the composite MR volume is then performed to correct intensity inhomogeneity in the composite MR volume. The volume normalization of the composite MR volume may be performed using template MR volume or without a template MR volume.

Abstract translation: 公开了一种用于自动磁共振（MR）体积组成和归一化的方法和系统。 在一个实施例中，接收多个MR卷。 从多个MR卷生成复合MR卷。 然后执行复合MR体积的体积归一化以校正复合MR体积中的强度不均匀性。 可以使用模板MR体积或不具有模板MR体积来执行复合MR体积的体积归一化。

公开(公告)号：US20120296202A1

公开(公告)日：2012-11-22

申请号：US13475048

申请日：2012-05-18

Applicant: Peter Mountney ,  Markus Kaiser ,  Ingmar Voigt ,  Matthias John ,  Razvan Ioan Ionasec ,  Jan Boese ,  Dorin Comaniciu

Inventor： Peter Mountney ,  Markus Kaiser ,  Ingmar Voigt ,  Matthias John ,  Razvan Ioan Ionasec ,  Jan Boese ,  Dorin Comaniciu

IPC: A61B6/02 ,  A61B8/12

CPC classification number: A61B6/12 ,  A61B6/4417 ,  A61B6/4441 ,  A61B6/487 ,  A61B6/503 ,  A61B6/5247 ,  A61B8/0841 ,  A61B8/0883 ,  A61B8/12 ,  A61B8/4245 ,  A61B8/4416 ,  A61B8/5261

Abstract: A method and system for registering ultrasound images and physiological models to x-ray fluoroscopy images is disclosed. A fluoroscopic image and an ultrasound image, such as a Transesophageal Echocardiography (TEE) image, are received. A 2D location of an ultrasound probe is detected in the fluoroscopic image. A 3D pose of the ultrasound probe is estimated based on the detected 2D location of the ultrasound probe in the fluoroscopic image. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image based on the estimated 3D pose of the ultrasound probe. The ultrasound image can then be projected into the fluoroscopic image using a projection matrix associated with the fluoroscopic image. A patient specific physiological model can be detected in the ultrasound image and projected into the fluoroscopic image.

Abstract translation: 公开了一种用于将超声图像和生理模型记录到X射线透视图像的方法和系统。 接收透视图像和超声图像，如经食道超声心动图（TEE）图像。 在透视图像中检测超声探头的2D位置。 基于在荧光镜图像中检测到的超声波探头的2D位置来估计超声探头的3D姿态。 超声图像被映射到用于根据超声探头的估计3D姿势获取荧光镜图像的荧光镜图像采集装置的3D坐标系。 然后可以使用与透视图像相关联的投影矩阵将超声图像投影到透视图像中。 可以在超声图像中检测患者特异性生理模型并将其投影到透视图像中。

公开(公告)号：US20120232379A1

公开(公告)日：2012-09-13

申请号：US13416168

申请日：2012-03-09

Applicant: Razvan Ioan Ionasec ,  Dime Vitanovski ,  Alexey Tsymbal ,  Gareth Funka-Lea ,  Dorin Comaniciu ,  Andreas Greiser ,  Edgar Mueller

Inventor： Razvan Ioan Ionasec ,  Dime Vitanovski ,  Alexey Tsymbal ,  Gareth Funka-Lea ,  Dorin Comaniciu ,  Andreas Greiser ,  Edgar Mueller

IPC: A61B5/055

CPC classification number: G06T7/0083 ,  G06T7/12 ,  G06T2207/10088 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/30048

Abstract: A system and method for regression-based segmentation of the mitral valve in 2D+t cardiac magnetic resonance (CMR) slices is disclosed. The 2D+t CMR slices are acquired according to a mitral valve-specific acquisition protocol introduced herein. A set of mitral valve landmarks is detected in each 2D CMR slice and mitral valve contours are estimated in each 2D CMR slice based on the detected landmarks. A full mitral valve model is reconstructed from the mitral valve contours estimated in the 2D CMR slices using a trained regression model. Each 2D CMR slice may be a cine image acquired over a full cardiac cycle. In this case, the segmentation method reconstructs a patient-specific 4D dynamic mitral valve model from the 2D+t CMR image data.

Abstract translation: 公开了一种用于2D + t心脏磁共振（CMR）切片二尖瓣回归分割的系统和方法。 根据本文引入的二尖瓣特异性获取方案获取2D + t CMR切片。 在每个2D CMR切片中检测到一组二尖瓣地标，并且基于检测到的界标在每个2D CMR切片中估计二尖瓣轮廓。 使用训练有素的回归模型，从2D CMR切片中估算的二尖瓣轮廓重建完整的二尖瓣模型。 每个2D CMR切片可以是在整个心动周期上获取的电影图像。 在这种情况下，分割方法从2D + t CMR图像数据重建患者特定的4D动态二尖瓣模型。

公开(公告)号：US20120183193A1

公开(公告)日：2012-07-19

申请号：US13342248

申请日：2012-01-03

Applicant: Michael Wels ,  Michael Suehling ,  Shaohua Kevin Zhou ,  David Liu ,  Dijia Wu ,  Christopher V. Alvino ,  Michael Kelm ,  Grzegorz Soza ,  Dorin Comaniciu

Inventor： Michael Wels ,  Michael Suehling ,  Shaohua Kevin Zhou ,  David Liu ,  Dijia Wu ,  Christopher V. Alvino ,  Michael Kelm ,  Grzegorz Soza ,  Dorin Comaniciu

IPC: G06K9/00

CPC classification number: G06K9/4614 ,  G06K9/6256 ,  G06K2209/055 ,  G06K2209/40 ,  G06T7/0012 ,  G06T2207/10072 ,  G06T2207/20016 ,  G06T2207/20081 ,  G06T2207/30012 ,  G06T2207/30096

Abstract: A method and system for automatic detection and volumetric quantification of bone lesions in 3D medical images, such as 3D computed tomography (CT) volumes, is disclosed. Regions of interest corresponding to bone regions are detected in a 3D medical image. Bone lesions are detected in the regions of interest using a cascade of trained detectors. The cascade of trained detectors automatically detects lesion centers and then estimates lesion size in all three spatial axes. A hierarchical multi-scale approach is used to detect bone lesions using a cascade of detectors on multiple levels of a resolution pyramid of the 3D medical image.

Abstract translation: 公开了一种用于3D医学图像中的骨损伤的自动检测和体积定量的方法和系统，例如3D计算机断层摄影（CT）体积。 在3D医学图像中检测与骨骼区域相对应的兴趣区域。 使用级联的经过训练的检测器在感兴趣的区域中检测到骨损伤。 训练有素的检测器的级联自动检测损伤中心，然后估计所有三个空间轴的病变大小。 使用分级多尺度方法来使用3D医学图像的分辨率金字塔的多个级别上的级联检测器来检测骨损伤。

公开(公告)号：US20120123250A1

公开(公告)日：2012-05-17

申请号：US13297373

申请日：2011-11-16

Applicant: Yu Pang ,  Yefeng Zheng ,  Matthias John ,  Jan Boese ,  Dorin Comaniciu

Inventor： Yu Pang ,  Yefeng Zheng ,  Matthias John ,  Jan Boese ,  Dorin Comaniciu

IPC: A61B6/00

CPC classification number: A61B6/487 ,  A61B6/12 ,  A61B6/5264 ,  G06T7/246 ,  G06T7/277 ,  G06T2207/10121 ,  G06T2207/30021

Abstract: A method and system for autoregressive model based pigtail catheter motion prediction in a fluoroscopic image sequence is disclosed. Parameters of an autoregressive model are estimated based on observed pigtail catheter tip positions in a plurality of previous frames of a fluoroscopic image sequence. A pigtail catheter tip position in a current frame of the fluoroscopic image sequence is predicted using the fitted autoregressive model. The predicted pigtail catheter tip position can be used to constrain pigtail catheter tip detection in the current frame. The predicted pigtail catheter tip position may also be used to predict abnormal motion in the fluoroscopic image sequence.

Abstract translation: 公开了一种在透视图像序列中基于自回归模型的尾纤导管运动预测的方法和系统。 基于在透视图像序列的多个先前帧中的观察到的尾纤导管尖端位置来估计自回归模型的参数。 使用拟合的自回归模型预测荧光镜图像序列的当前帧中的尾纤导管尖端位置。 预测的猪尾导管尖端位置可用于限制当前帧中的尾纤导管尖端检测。 预测的猪尾导管尖端位置也可以用于预测荧光镜像图像序列中的异常运动。