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使用非刚性图像配准构建用于心脏运动分析的统计模型。

Construction of a statistical model for cardiac motion analysis using nonrigid image registration.

作者信息

Chandrashekara Raghavendra, Rao Anil, Sanchez-Ortiz Gerardo Ivar, Mohiaddin Raad H, Rueckert Daniel

机构信息

Visual Information Processing Group, Department of Computing, Imperial College of Science, Technology, and Medicine, 180 Queen's Gate, London SW7 2BZ, UK.

出版信息

Inf Process Med Imaging. 2003 Jul;18:599-610. doi: 10.1007/978-3-540-45087-0_50.

Abstract

In this paper we present a new technique for tracking the movement of the myocardium using a statistical model derived from the motion fields in the hearts of several healthy volunteers. To build the statistical model we tracked the motion of the myocardium in 17 volunteers using a nonrigid registration technique based on free-form deformations and mapped the motion fields obtained into a common reference coordinate system. A principal component analysis (PCA) was then performed on the motion fields to extract the major modes of variation in the fields between the successive time frames. The modes of variation obtained were then used to parametrize the free-form deformations and build our statistical model. The results of using our model to track the motion of the heart in normal volunteers are also presented.

摘要

在本文中,我们提出了一种新技术,该技术利用从多名健康志愿者心脏中的运动场导出的统计模型来跟踪心肌的运动。为了构建统计模型,我们使用基于自由形式变形的非刚性配准技术跟踪了17名志愿者心肌的运动,并将获得的运动场映射到一个公共参考坐标系中。然后对运动场进行主成分分析(PCA),以提取连续时间帧之间场中变化的主要模式。然后,将获得的变化模式用于参数化自由形式变形并构建我们的统计模型。本文还展示了使用我们的模型跟踪正常志愿者心脏运动的结果。

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