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In-vivo Motion Analysis of Bi-ventricular Hearts from Tagged MR Images

机译:标记MR图像对双心室心脏的体内运动分析

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We conduct experiments to look at the in-vivo cardiac motion during systole, to visualize heart contraction, and toexamine the clinical usefulness. Our model-based technique incorporates subject-specific modeling, motion analysisand the extraction of clinically relevant parameters within one framework. Previous bi-ventricular model based methodcould only handle up to the mid-ventricles and have a few test-subjects. Our parameterized model includes the LV,RV and up to the basal area for full ventricular motion study. Finite element methods capture cardiac motion bytracking the material points from tagged Magnetic Resonance (MR) images. A number of experiments from ten subjectsare evaluated and analyzed. We tested subject several times and compared the resulting parameters to ensure thereproducibility and deviations. The resulting parameters can be used to describe the cardiac motion of normalsubjects. The patterns of normal subjects were derived from experiments. While significant shape and motion variationswere apparent in normal subjects, the quantitative analysis show typical patterns. Generally, the basal area movesdownwards and the apical area contracts towards the cavity. The principal strain analysis describes the directions andmagnitudes of maximum shortening, and maximum thickening.
机译:我们进行实验以观察收缩期的体内心脏运动,以可视化心脏收缩,并检查其临床实用性。我们基于模型的技术将特定主题的建模,运动分析和临床相关参数的提取整合到一个框架中。以前的基于双心室模型的方法只能处理心室中部,并且有一些测试对象。我们的参数化模型包括LV,RV以及直至基础区域的完整心室运动研究。有限元方法通过跟踪标记的磁共振(MR)图像中的物质点来捕获心脏运动。对来自十个受试者的许多实验进行了评估和分析。我们对受试者进行了多次测试,并比较了产生的参数,以确保可重复性和偏差。结果参数可用于描述正常对象的心脏运动。正常受试者的模式来自实验。在正常人中,明显的形状和运动变化明显,但定量分析显示出典型的模式。通常,基底区域向下移动,而顶端区域向空腔收缩。主应变分析描述了最大缩短和最大增厚的方向和幅度。

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    Department of Computer and Information Science University of Pennsylvania Philadelphia PA 19104 USA kypark@graphics.cis.upenn.edu;

    Department of Radiology New York University School of Medicine New York NY10016 USA Leon.axel@med.nyu.edu;

    CBIM Center Computer Science Department and Bioengineering Department Rutgers University New Brunswick NJ08854 USA dnm@cs.rutgers.edu;

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  • 入库时间 2022-08-26 14:39:30

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