利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

166 条记录 (耗时 0.031 秒)

    Method and System for Patient Specific Planning of Cardiac Therapies on Preoperative Clinical Data and Medical Images
    81.
    发明申请
    Method and System for Patient Specific Planning of Cardiac Therapies on Preoperative Clinical Data and Medical Images 有权
    心脏手术患者术前临床资料和医学图像的方法与系统

    公开(公告)号：US20130197881A1

    公开(公告)日：2013-08-01

    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

    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.

    摘要翻译： 公开了基于术前临床数据和医学图像（例如ECG数据，磁共振成像（MRI）数据和超声数据）的心脏治疗的患者特异性规划的方法和系统，例如心脏再同步治疗（CRT） 。 从患者的医学图像数据产生左心室和右心室的患者特异性解剖模型。 基于左心室和右心室的患者特异性解剖模型和临床数据生成包括心脏电生理学，生物力学和血液动力学的患者特异性计算心脏模型。 使用患者特异性计算心脏模型进行心脏治疗的模拟，例如在一个或多个解剖位置处的CRT。 然后从患者特异性模型计算临床心脏参数的变化，构成对治疗计划和优化有用的治疗结果的预测因子。

    Method and system for left ventricle detection in 2D magnetic resonance images using ranking based multi-detector aggregation
    82.
    发明授权
    Method and system for left ventricle detection in 2D magnetic resonance images using ranking based multi-detector aggregation 有权
    使用基于排序的多检测器聚合的2D磁共振图像中左心室检测的方法和系统

    公开(公告)号：US08340385B2

    公开(公告)日：2012-12-25

    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： G06K9/00

    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。

    Method and System for Detection of Contrast Injection Fluoroscopic Image Sequences
    83.
    发明申请
    Method and System for Detection of Contrast Injection Fluoroscopic Image Sequences 审中-公开
    对比注射荧光检查图像序列检测方法与系统

    公开(公告)号：US20120257807A1

    公开(公告)日：2012-10-11

    申请号：US13455619

    申请日：2012-04-25

    申请人： Benjamin J. Sapp ,  Wei Zhang ,  Bogdan Georgescu ,  Simone Prummer ,  Dorin Comaniciu

    发明人： Benjamin J. Sapp ,  Wei Zhang ,  Bogdan Georgescu ,  Simone Prummer ,  Dorin Comaniciu

    IPC分类号： G06K9/00

    CPC分类号： G06K9/00 ,  G06K2209/05 ,  G06T7/0012

    摘要： A method and system for detecting a spatial and temporal location of a contrast injection in a fluoroscopic image sequence is disclosed. Training volumes generated by stacking a sequence of 2D fluoroscopic images in time order are annotated with ground truth contrast injection points. A heart rate is globally estimated for each training volume, and local frequency and phase is estimated in a neighborhood of the ground truth contrast injection point for each training volume. Frequency and phase invariant features are extracted from each training volume based on the heart rate, local frequency and phase, and a detector is trained based on the training volumes and the features extracted for each training volume. The detector can be used to detect the spatial and temporal location of a contrast injection in a fluoroscopic image sequence.

    摘要翻译： 公开了一种用于检测透视图像序列中的对比度注入的空间和时间位置的方法和系统。 通过以时间顺序堆叠一系列2D透视图像而产生的训练体积用地面真实对比度注入点注释。 对于每个训练体积，全局估计心率，并且在每个训练体积的地面真实对比度注入点的邻域中估计局部频率和相位。 基于心率，局部频率和相位从每个训练体积中提取频率和相位不变特征，并且基于训练量和针对每个训练体积提取的特征来训练检测器。 检测器可用于检测透视图像序列中对比度注入的空间和时间位置。

    Method and system for left ventricle endocardium surface segmentation using constrained optimal mesh smoothing
    85.
    发明授权
    Method and system for left ventricle endocardium surface segmentation using constrained optimal mesh smoothing 失效
    使用约束最优网格平滑的左心室心内膜表面分割的方法和系统

    公开(公告)号：US08150119B2

    公开(公告)日：2012-04-03

    申请号：US12319686

    申请日：2009-01-09

    申请人： Yefeng Zheng ,  Bogdan Georgescu ,  Michael Scheuering ,  Dorin Comaniciu

    发明人： Yefeng Zheng ,  Bogdan Georgescu ,  Michael Scheuering ,  Dorin Comaniciu

    IPC分类号： G06K9/34 ,  G06T15/00

    CPC分类号： G06K9/34 ,  G06K2209/051 ,  G06T7/12 ,  G06T7/149 ,  G06T2207/10081 ,  G06T2207/20124 ,  G06T2207/30048

    摘要： A method and system for left ventricle (LV) endocardium surface segmentation using constrained optimal mesh smoothing is disclosed. The LV endocardium surface in the 3D cardiac volume is initially segmented in a 3D cardiac volume, such as a CT volume, resulting in an LV endocardium surface mesh. A smoothed LV endocardium surface mesh is generated by smoothing the LV endocardium surface mesh using constrained optimal mesh smoothing. The constrained optimal mesh smoothing determines an optimal adjustment for each point on the LV endocardium surface mesh by minimizing an objective function based at least on a smoothness measure, subject to a constraint bounding the adjustment for each point. The adjustment for each point can be constrained to prevent adjustments inward toward the blood pool in order to ensure that the smoothed LV endocardium surface mesh encloses the entire blood pool.

    摘要翻译： 公开了使用约束最优网格平滑的左心室（LV）心内膜表面分割的方法和系统。 3D心脏体积中的LV心内膜表面最初在3D心脏体积（例如CT体积）中分段，导致LV心内膜表面网。 通过使用约束最优网格平滑平滑LV心内膜表面网格来生成平滑的LV心内膜表面网格。 受约束的最优网格平滑通过使至少基于平滑度量度的目标函数最小化来限制LV心内膜表面网格上的每个点的最佳调整，受限于对每个点的调整。 可以限制每个点的调整以防止向血液向内的调节，以确保平滑的LV心内膜表面网格包围整个血液池。

    Method and System for Detecting 3D Anatomical Structures Using Constrained Marginal Space Learning
    88.
    发明申请
    Method and System for Detecting 3D Anatomical Structures Using Constrained Marginal Space Learning 有权
    使用约束边际空间学习检测3D解剖结构的方法和系统

    公开(公告)号：US20090304251A1

    公开(公告)日：2009-12-10

    申请号：US12471761

    申请日：2009-05-26

    申请人： Yefeng Zheng ,  Bogdan Georgescu ,  Haibin Ling ,  Michael Scheuering ,  Dorin Comaniciu

    发明人： Yefeng Zheng ,  Bogdan Georgescu ,  Haibin Ling ,  Michael Scheuering ,  Dorin Comaniciu

    IPC分类号： G06K9/00

    CPC分类号： G06K9/3233 ,  G06K2209/051 ,  G06T7/75 ,  G06T2207/10081 ,  G06T2207/30004

    摘要： A method and apparatus for detecting 3D anatomical objects in medical images using constrained marginal space learning (MSL) is disclosed. A constrained search range is determined for an input medical image volume based on training data. A first trained classifier is used to detect position candidates in the constrained search range. Position-orientation hypotheses are generated from the position candidates using orientation examples in the training data. A second trained classifier is used to detect position-orientation candidates from the position-orientation hypotheses. Similarity transformation hypotheses are generated from the position-orientation candidates based on scale examples in the training data. A third trained classifier is used to detect similarity transformation candidates from the similarity transformation hypotheses, and the similarity transformation candidates define the position, translation, and scale of the 3D anatomic object in the medical image volume.

    摘要翻译： 公开了一种使用受限边际空间学习（MSL）检测医学图像中3D解剖学对象的方法和装置。 基于训练数据确定输入医学图像体积的约束搜索范围。 第一训练分类器用于检测约束搜索范围内的位置候选。 使用训练数据中的取向示例从位置候选者生成位置取向假设。 第二训练分类器用于从位置定向假设检测位置方向候选。 基于训练数据中的比例示例，从位置定位候选生成相似度转换假设。 第三训练分类器用于从相似变换假设检测相似变换候选，并且相似变换候选定义医学图像体积中的3D解剖对象的位置，平移和比例。