-
1.发明申请SYSTEM AND METHOD FOR AUTOMATIC MAGNETIC RESONANCE VOLUME COMPOSITION AND NORMALIZATION 有权用于自动磁共振体积组成和正规化的系统和方法公开(公告)号：US20130072782A1
公开(公告)日：2013-03-21
申请号：US13616548
申请日：2012-09-14
申请人： David Liu , Shaohua Kevin Zhou , Peter Gall , Dorin Comaniciu , Andre de Aliveira , Berthold Kiefer
发明人： David Liu , Shaohua Kevin Zhou , Peter Gall , Dorin Comaniciu , Andre de Aliveira , Berthold Kiefer
IPC分类号： A61B5/055
CPC分类号： G06T7/0034 , A61B5/055 , G01R33/5608 , G01R33/565 , G06T7/35 , G06T2207/10088 , G06T2207/30004
摘要： 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.
摘要翻译： 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。
-
2.发明授权System and method for automatic magnetic resonance volume composition and normalization 有权用于自动磁共振体积组成和归一化的系统和方法公开(公告)号：US09147250B2
公开(公告)日：2015-09-29
申请号：US13616548
申请日：2012-09-14
申请人： David Liu , Shaohua Kevin Zhou , Peter Gall , Dorin Comaniciu , Andre de Oliveira , Berthold Kiefer
发明人： David Liu , Shaohua Kevin Zhou , Peter Gall , Dorin Comaniciu , Andre de Oliveira , Berthold Kiefer
IPC分类号： A61B5/055 , G06T7/00 , G01R33/56 , G01R33/565
CPC分类号： G06T7/0034 , A61B5/055 , G01R33/5608 , G01R33/565 , G06T7/35 , G06T2207/10088 , G06T2207/30004
摘要： 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.
摘要翻译： 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。
-
3.发明授权Method and system for automatic prostate segmentation in magnetic resonance images 有权磁共振图像自动前列腺分割的方法和系统公开(公告)号：US09269156B2
公开(公告)日：2016-02-23
申请号：US13947300
申请日：2013-07-22
申请人： Neil Birkbeck , Jingdan Zhang , Martin Requardt , Berthold Kiefer , Peter Gall , Shaohua Kevin Zhou
发明人： Neil Birkbeck , Jingdan Zhang , Martin Requardt , Berthold Kiefer , Peter Gall , Shaohua Kevin Zhou
CPC分类号： G06T7/0081 , G06T5/009 , G06T7/11 , G06T7/149 , G06T2200/04 , G06T2207/10088 , G06T2207/20016 , G06T2207/20081 , G06T2207/30081
摘要： A method and system for fully automatic segmentation the prostate in magnetic resonance (MR) image data is disclosed. Intensity normalization is performed on an MR image of a patient to adjust for global contrast changes between the MR image and other MR scans and to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image. An initial prostate segmentation in the MR image is obtained by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL). The initial prostate segmentation is refined using one or more trained boundary classifiers.
摘要翻译： 公开了一种在磁共振（MR）图像数据中全息自动分割前列腺的方法和系统。 对患者的MR图像进行强度归一化，以调整MR图像和其他MR扫描之间的全局对比度变化，并且由于用于获取MR图像的直肠内线圈来调整MR图像内的强度变化。 通过使用边缘空间学习（MSL）将前列腺的学习统计形状模型与MR图像对准，获得MR图像中的初始前列腺分割。 使用一个或多个训练有素的边界分类器来改进初始前列腺分割。
-
4.发明申请Method and System for Automatic Prostate Segmentation in Magnetic Resonance Images 有权磁共振图像中自动前列腺分割的方法与系统公开(公告)号：US20140029823A1
公开(公告)日：2014-01-30
申请号：US13947300
申请日：2013-07-22
申请人： Neil Birkbeck , Jingdan Zhang , Martin Requardt , Berthold Kiefer , Peter Gall , Shaohua Kevin Zhou
发明人： Neil Birkbeck , Jingdan Zhang , Martin Requardt , Berthold Kiefer , Peter Gall , Shaohua Kevin Zhou
IPC分类号： G06T7/00
CPC分类号： G06T7/0081 , G06T5/009 , G06T7/11 , G06T7/149 , G06T2200/04 , G06T2207/10088 , G06T2207/20016 , G06T2207/20081 , G06T2207/30081
摘要： A method and system for fully automatic segmentation the prostate in magnetic resonance (MR) image data is disclosed. Intensity normalization is performed on an MR image of a patient to adjust for global contrast changes between the MR image and other MR scans and to adjust for intensity variation within the MR image due to an endorectal coil used to acquire the MR image. An initial prostate segmentation in the MR image is obtained by aligning a learned statistical shape model of the prostate to the MR image using marginal space learning (MSL). The initial prostate segmentation is refined using one or more trained boundary classifiers.
摘要翻译： 公开了一种在磁共振（MR）图像数据中全息自动分割前列腺的方法和系统。 对患者的MR图像进行强度归一化，以调整MR图像和其他MR扫描之间的全局对比度变化，并且由于用于获取MR图像的直肠内线圈来调整MR图像内的强度变化。 通过使用边缘空间学习（MSL）将前列腺的学习统计形状模型与MR图像对准，获得MR图像中的初始前列腺分割。 使用一个或多个训练有素的边界分类器来改进初始前列腺分割。
-
5.发明申请Method and System for Automatic Lung Segmentation in Magnetic Resonance Imaging Videos 有权磁共振成像视频中自动肺分割的方法与系统公开(公告)号：US20130121545A1
公开(公告)日：2013-05-16
申请号：US13678916
申请日：2012-11-16
IPC分类号： G06T7/00
CPC分类号： G06T7/0014 , G06T7/12 , G06T7/143 , G06T7/149 , G06T2207/10076 , G06T2207/20076 , G06T2207/20081 , G06T2207/20124 , G06T2207/30061
摘要： A method and system for automatic lung segmentation in magnetic resonance imaging (MRI) images and videos is disclosed. A plurality of predetermined key landmarks of a lung are detected in an MRI image. The key landmarks may be detected using discriminative joint contexts representing combinations of multiple key landmarks. A lung boundary is segmented in the MRI image based on the detected key landmarks. The landmark detection and the lung boundary segmentation can be repeated in each frame of an MRI video.
摘要翻译： 公开了磁共振成像（MRI）图像和视频中自动肺分割的方法和系统。 在MRI图像中检测肺的多个预定键标记。 可以使用表示多个关键地标的组合的区分联合上下文来检测关键地标。 基于检测到的关键地标，在MRI图像中分割肺边界。 可以在MRI视频的每个帧中重复地标检测和肺边界分割。
-
6.发明授权Method and system for automatic lung segmentation in magnetic resonance imaging videos 有权磁共振成像视频中自动肺分割的方法和系统公开(公告)号：US08879810B2
公开(公告)日：2014-11-04
申请号：US13678916
申请日：2012-11-16
CPC分类号： G06T7/0014 , G06T7/12 , G06T7/143 , G06T7/149 , G06T2207/10076 , G06T2207/20076 , G06T2207/20081 , G06T2207/20124 , G06T2207/30061
摘要： A method and system for automatic lung segmentation in magnetic resonance imaging (MRI) images and videos is disclosed. A plurality of predetermined key landmarks of a lung are detected in an MRI image. The key landmarks may be detected using discriminative joint contexts representing combinations of multiple key landmarks. A lung boundary is segmented in the MRI image based on the detected key landmarks. The landmark detection and the lung boundary segmentation can be repeated in each frame of an MRI video.
摘要翻译： 公开了磁共振成像（MRI）图像和视频中自动肺分割的方法和系统。 在MRI图像中检测肺的多个预定键标记。 可以使用表示多个关键地标的组合的区分联合上下文来检测关键地标。 基于检测到的关键地标，在MRI图像中分割肺边界。 可以在MRI视频的每个帧中重复地标检测和肺边界分割。
-
7.发明授权Systems and method for automatic prostate localization in MR images using random walker segmentation initialized via boosted classifiers 有权使用通过增强分类器初始化的随机步行器分割的MR图像中的自动前列腺定位的系统和方法公开(公告)号：US08811701B2
公开(公告)日：2014-08-19
申请号：US13291615
申请日：2011-11-08
申请人： Parmeshwar Khurd , Leo Grady , Ali Kamen , Mamadou Diallo , Kalpitkumar Gajera , Peter Gall , Martin Requardt , Berthold Kiefer , Clifford R. Weiss
发明人： Parmeshwar Khurd , Leo Grady , Ali Kamen , Mamadou Diallo , Kalpitkumar Gajera , Peter Gall , Martin Requardt , Berthold Kiefer , Clifford R. Weiss
CPC分类号： G06K9/6226 , G06T7/11 , G06T7/143 , G06T7/194 , G06T2207/10088 , G06T2207/20081 , G06T2207/20156 , G06T2207/30081
摘要： Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.
摘要翻译： T2加权MR图像中的自动前列腺定位有利于劳动密集型癌症成像技术。 提供了准确分割MR图像前列腺的方法和系统，并解决了前列腺解剖和疾病，强度不均匀性以及直肠内线圈引起的假象的巨大变化。 用基于强度的多级高斯混合模型预期最大化（GMM-EM）原始MR图像分段训练的增强分类器自动检测前列腺的中心。 形状模型与多标签随机Walker（MLRW）结合使用，以限制MLRW内的播种过程。
-
8.发明申请Systems and Method for Automatic Prostate Localization in MR Images Using Random Walker Segmentation Initialized Via Boosted Classifiers 有权使用随机步行器分割的MR图像中自动前列腺定位的系统和方法通过增强分类器初始化公开(公告)号：US20130064439A1
公开(公告)日：2013-03-14
申请号：US13291615
申请日：2011-11-08
申请人： Parmeshwar Khurd , Leo Grady , Ali Kamen , Mamadou Diallo , Kalpitkumar Gajera , Peter Gall , Martin Requardt , Berthold Kiefer , Clifford R. Weiss
发明人： Parmeshwar Khurd , Leo Grady , Ali Kamen , Mamadou Diallo , Kalpitkumar Gajera , Peter Gall , Martin Requardt , Berthold Kiefer , Clifford R. Weiss
CPC分类号： G06K9/6226 , G06T7/11 , G06T7/143 , G06T7/194 , G06T2207/10088 , G06T2207/20081 , G06T2207/20156 , G06T2207/30081
摘要： Automatic prostate localization in T2-weighted MR images facilitate labor-intensive cancer imaging techniques. Methods and systems to accurately segment the prostate gland in MR images are provided and address large variations in prostate anatomy and disease, intensity inhomogeneities, and artifacts induced by endorectal coils. A center of the prostate is automatically detected with a boosted classifier trained on intensity based multi-level Gaussian Mixture Model Expectation Maximization (GMM-EM) segmentations of the raw MR images. A shape model is used in conjunction with Multi-Label Random Walker (MLRW) to constrain the seeding process within MLRW.
摘要翻译： T2加权MR图像中的自动前列腺定位有利于劳动密集型癌症成像技术。 提供了准确分割MR图像前列腺的方法和系统，并解决了前列腺解剖和疾病，强度不均匀性以及直肠内线圈引起的假象的巨大变化。 用基于强度的多级高斯混合模型预期最大化（GMM-EM）原始MR图像分段训练的增强分类器自动检测前列腺的中心。 形状模型与多标签随机Walker（MLRW）结合使用，以限制MLRW内的播种过程。
-
公开(公告)号：US20120041909A1
公开(公告)日：2012-02-16
申请号：US13207564
申请日：2011-08-11
IPC分类号： G06F15/18
CPC分类号： G06F19/325 , G01R33/543 , G01R33/546 , G06F19/00
摘要： A database contains variants of protocols for the operation of magnetic resonance tomographs as well as different types of magnetic resonance tomographs. Each variant contains parameter values and is associated with one of the types. In a training phase, relationships are determined between the parameters among one another and/or between the parameters and the associated types and are stored as patterns in a knowledge base. A protocol plan for the operation of a new magnetic resonance tomograph is created later in an application phase using the determined pattern. The method offers the advantage that the efficiency and quality of the automatic conversion of the protocols is improved. The improved quality of the protocol plan reduces operating time and costs for a manual post-processing of the protocols. Furthermore, a higher consistency of the protocols among one another is achieved both between product families and between individual configurations.
摘要翻译： 数据库包含用于操作磁共振断层扫描仪以及不同类型的磁共振断层扫描仪的协议的变体。 每个变体都包含参数值，并与其中一个类型相关联。 在训练阶段，在参数之间和/或参数与相关联的类型之间确定关系，并将其作为模式存储在知识库中。 在使用确定的模式的应用阶段中稍后创建用于新的磁共振断层摄影术的操作的协议计划。 该方法具有提高协议的自动转换的效率和质量的优点。 协议计划的改进质量减少了手动后处理协议的操作时间和成本。 此外，在产品系列之间和各个配置之间实现了彼此之间更高协议的一致性。
-
10.发明授权公开(公告)号：US07747313B2
公开(公告)日：2010-06-29
申请号：US11112073
申请日：2005-04-22
申请人： Berthold Kiefer , Niels Oesingmann , Alto Stemmer
发明人： Berthold Kiefer , Niels Oesingmann , Alto Stemmer
IPC分类号： A61B5/05
CPC分类号： A61B5/7207 , A61B5/055 , A61B5/7285
摘要： In a method for acquiring dynamically varying magnetic resonance signals, a respiration cycle of a patient is monitored in a learning phase. Acquisition of varying signals ensues with the highest possible temporal resolution in an initial phase with a breath-hold by the patient. Slowly varying signals are subsequently acquired with lower temporal resolution in a movement phase and with free respiration of the patient. The signal acquisitions are initiated by a pre-established trigger condition.
摘要翻译： 在用于获取动态变化的磁共振信号的方法中，在学习阶段监视患者的呼吸周期。 随着患者的呼吸暂停，在初始阶段采集变化的信号随之发生最高可能的时间分辨率。 随后在运动阶段和患者的自由呼吸中以较低的时间分辨率采集慢变信号。 信号采集由预先建立的触发条件启动。
-
-
-
-
-
-
-
-
-
搜索结果
34 条记录 (耗时 0.028 秒)
