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    Deformable Motion Correction for Stent Visibility Enhancement
    1.
    发明申请
    Deformable Motion Correction for Stent Visibility Enhancement 有权
    用于支架可见度增强的可变形运动校正

    公开(公告)号：US20120140998A1

    公开(公告)日：2012-06-07

    申请号：US13277312

    申请日：2011-10-20

    申请人： Ying Zhu ,  Christophe Chefd'hotel

    发明人： Ying Zhu ,  Christophe Chefd'hotel

    IPC分类号： G06K9/00

    CPC分类号： G06T5/006 ,  G06T7/194 ,  G06T2207/10081 ,  G06T2207/10121 ,  G06T2207/10132 ,  G06T2207/30021 ,  G06T2207/30101 ,  G06T2207/30204

    摘要： A method for enhancing stent visibility includes acquiring a set of image frames including multiple test frames. A set of measurement points uniformly distributed within an image of a stent is defined in the test frames. A local image context is defined around each measurement point. A non-rigid deformation field relating the local image contexts of the test frames to local image contexts of a reference image is calculated. The non-rigid deformation field is optimized by maximizing a similarity function between the local image contexts of the test frames and the local image contexts of the reference image. The optimized non-rigid deformation field is used to deform images of a stent in the multiple test frames and combine the non-rigidly deformed images of the stent from the test frames. An image frame with the combined image of the stents superimposed thereon is displayed.

    摘要翻译： 一种用于增强支架可见度的方法包括获取包括多个测试框架的一组图像帧。 在测试框架中定义了在支架的图像内均匀分布的一组测量点。 在每个测量点周围定义一个局部图像上下文。 计算将测试帧的局部图像上下文与参考图像的局部图像上下文相关联的非刚性变形场。 通过最大化测试帧的局部图像上下文与参考图像的局部图像上下文之间的相似度函数来优化非刚性变形场。 优化的非刚性变形场用于在多个测试框架中变形支架的图像，并将来自测试框架的支架的非刚性变形图像组合。 显示具有叠加在其上的支架的组合图像的图像帧。

    Deformable motion correction for stent visibility enhancement
    2.
    发明授权
    Deformable motion correction for stent visibility enhancement 有权
    支架可见度增强的变形运动校正

    公开(公告)号：US08515146B2

    公开(公告)日：2013-08-20

    申请号：US13277312

    申请日：2011-10-20

    申请人： Ying Zhu ,  Christophe Chefd'hotel

    发明人： Ying Zhu ,  Christophe Chefd'hotel

    IPC分类号： G06K9/00 ,  A61B1/00

    CPC分类号： G06T5/006 ,  G06T7/194 ,  G06T2207/10081 ,  G06T2207/10121 ,  G06T2207/10132 ,  G06T2207/30021 ,  G06T2207/30101 ,  G06T2207/30204

    摘要： A method for enhancing stent visibility includes acquiring a set of image frames including multiple test frames. A set of measurement points uniformly distributed within an image of a stent is defined in the test frames. A local image context is defined around each measurement point. A non-rigid deformation field relating the local image contexts of the test frames to local image contexts of a reference image is calculated. The non-rigid deformation field is optimized by maximizing a similarity function between the local image contexts of the test frames and the local image contexts of the reference image. The optimized non-rigid deformation field is used to deform images of a stent in the multiple test frames and combine the non-rigidly deformed images of the stent from the test frames. An image frame with the combined image of the stents superimposed thereon is displayed.

    摘要翻译： 一种用于增强支架可见度的方法包括获取包括多个测试框架的一组图像帧。 在测试框架中定义了在支架的图像内均匀分布的一组测量点。 在每个测量点周围定义局部图像上下文。 计算将测试帧的局部图像上下文与参考图像的局部图像上下文相关联的非刚性变形场。 通过最大化测试帧的局部图像上下文与参考图像的局部图像上下文之间的相似度函数来优化非刚性变形场。 优化的非刚性变形场用于在多个测试框架中变形支架的图像，并将来自测试框架的支架的非刚性变形图像组合。 显示具有叠加在其上的支架的组合图像的图像帧。

    Method and system for validating image registration
    4.
    发明授权
    Method and system for validating image registration 有权
    验证图像配准的方法和系统

    公开(公告)号：US08582846B2

    公开(公告)日：2013-11-12

    申请号：US13161656

    申请日：2011-06-16

    申请人： Guyu Li ,  Li Zhang ,  Christophe Chefd'hotel ,  Christoph Guetter

    发明人： Guyu Li ,  Li Zhang ,  Christophe Chefd'hotel ,  Christoph Guetter

    IPC分类号： G06K9/32

    CPC分类号： G06T7/37 ,  G06T2207/20056

    摘要： A method for validating non-rigid image registration includes acquiring a source image and a target image. Registration is performed from source image to target image using a non-rigid registration technique to produce forward transformation map. Registration is performed from the target image back to the source image using the non-rigid registration technique to produce a backward transformation map. Consistency registration error is measured as an indication of a change in local volume of the source with respect to the target image using the produced forward transformation map and the produced backward transformation map. The non-rigid registration technique is validated based on the measured consistency registration error.

    摘要翻译： 用于验证非刚性图像配准的方法包括获取源图像和目标图像。 使用非刚性注册技术从源图像到目标图像进行注册以产生正向变换图。 使用非刚性登记技术从目标图像返回到源图像，从而产生反向变换图。 使用所产生的正向变换图和所产生的向后变换图，测量一致性登记误差作为相对于目标图像的源的局部体积的变化的指示。 基于测量的一致性注册误差验证非刚性注册技术。

    Automatic Initialization for 2D/3D Registration
    6.
    发明申请
    Automatic Initialization for 2D/3D Registration 有权
    2D / 3D注册自动初始化

    公开(公告)号：US20130051647A1

    公开(公告)日：2013-02-28

    申请号：US13591752

    申请日：2012-08-22

    申请人： Shun Miao ,  Christophe Chefd'hotel ,  Rui Liao

    发明人： Shun Miao ,  Christophe Chefd'hotel ,  Rui Liao

    IPC分类号： G06K9/62

    CPC分类号： G06T7/344 ,  G06T2207/10081 ,  G06T2207/10121

    摘要： A method for automatic initialization of 2D to 3D image registration includes acquiring a 3D model. A plurality of shape descriptor features is calculated from the acquired 3D model representing a plurality of poses of the 3D model. A 2D image is acquired. The plurality of shape descriptors is matched to the acquired 2D model. An optimum pose of the 3D model is determined based on the matching of the plurality of shape descriptors to the acquired 2D model. An initial registration is generated, in an image processing system, between the 3D model and the 2D image based on the determined optimum pose.

    摘要翻译： 用于2D到3D图像配准的自动初始化的方法包括获取3D模型。 从所获取的3D模型中计算出多个形状描述符特征，该3D模型表示3D模型的多个姿态。 获取2D图像。 多个形状描述符与获取的2D模型相匹配。 基于多个形状描述符与所获取的2D模型的匹配来确定3D模型的最佳姿态。 在图像处理系统中，基于确定的最佳姿态在3D模型和2D图像之间生成初始注册。

    Robust classification of fat and water images from 1-point-Dixon reconstructions
    7.
    发明授权
    Robust classification of fat and water images from 1-point-Dixon reconstructions 有权
    从1点狄克逊重建的肥胖和水图像的鲁棒分类

    公开(公告)号：US08064674B2

    公开(公告)日：2011-11-22

    申请号：US12467614

    申请日：2009-05-18

    申请人： Matthias Fenchel ,  Christophe Chefd'hotel

    发明人： Matthias Fenchel ,  Christophe Chefd'hotel

    IPC分类号： G06K9/62 ,  G06T7/00 ,  G06N5/02

    CPC分类号： G06K9/6292 ,  G06T7/11 ,  G06T2207/10088

    摘要： Dixon methods in magnetic resonance imaging generate MRI images that may contain at least two tissue components such as fat and water. Dixon methods generate images containing both tissue components and predominantly one tissue component. A first segmentation of a first tissue component is generated in a T1 weighted image. The segmentation is correlated with at least a first and a second Dixon image. The image with the highest correlation is assigned the first tissue component.

    摘要翻译： 磁共振成像中的Dixon方法产生可能含有至少两种组织成分如脂肪和水的MRI图像。 Dixon方法产生包含组织成分和主要是一个组织成分的图像。 在T1加权图像中产生第一组织分量的第一分割。 分割与至少第一和第二Dixon图像相关。 具有最高相关性的图像被分配第一组织分量。

    Robust Classification of Fat and Water Images from 1-point-Dixon Reconstructions
    8.
    发明申请
    Robust Classification of Fat and Water Images from 1-point-Dixon Reconstructions 有权
    1点狄克逊重建中脂肪和水分图像的鲁棒分类

    公开(公告)号：US20100111390A1

    公开(公告)日：2010-05-06

    申请号：US12467614

    申请日：2009-05-18

    申请人： Matthias Fenchel ,  Christophe Chefd'hotel

    发明人： Matthias Fenchel ,  Christophe Chefd'hotel

    IPC分类号： G06K9/62 ,  G06T7/00 ,  G06N5/02

    CPC分类号： G06K9/6292 ,  G06T7/11 ,  G06T2207/10088

    摘要： Dixon methods in magnetic resonance imaging generate MRI images that may contain at least two tissue components such as fat and water. Dixon methods generate images containing both tissue components and predominantly one tissue component. A first segmentation of a first tissue component is generated in a T1 weighted image. The segmentation is correlated with at least a first and a second Dixon image. The image with the highest correlation is assigned the first tissue component.

    摘要翻译： 磁共振成像中的Dixon方法产生可能含有至少两种组织成分如脂肪和水的MRI图像。 Dixon方法产生包含组织成分和主要是一个组织成分的图像。 在T1加权图像中产生第一组织分量的第一分割。 分割与至少第一和第二Dixon图像相关。 具有最高相关性的图像被分配第一组织分量。