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State-Space Analysis of Cardiac Motion With Biomechanical Constraints

机译:具有生物力学约束的心脏运动的状态空间分析

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Quantitative estimation of nonrigid motion from image sequences has important technical and practical significance. State-space analysis provides powerful and convenient ways to construct and incorporate the physically meaningful system dynamics of an object, the image-derived observations, and the process and measurement noise disturbances. In this paper, we present a biomechanical-model constrained state-space analysis framework for the multiframe estimation of the periodic cardiac motion and deformation. The physical constraints take the roles as spatial regulator of the myocardial behavior and spatial filter/interpolator of the data measurements, while techniques from statistical filtering theory impose spatiotemporal constraints to facilitate the incorporation of multiframe information to generate optimal estimates of the heart kinematics. Physiologically meaningful results have been achieved from estimated displacement fields and strain maps using in vivo left ventricular magnetic resonance tagging and phase contrast image sequences, which provide the tag-tag and tag-boundary displacement inputs, and the mid-wall instantaneous velocity information and boundary displacement measures, respectively
机译:从图像序列定量估计非刚性运动具有重要的技术和实践意义。状态空间分析提供了强大而便捷的方法来构造和合并对象的物理上有意义的系统动力学,图像派生的观测值以及过程和测量噪声干扰。在本文中,我们提出了一种生物力学模型约束状态空间分析框架,用于周期性心动和变形的多帧估计。物理约束起着心肌行为的空间调节器和数据测量的空间滤波/内插器的作用,而统计滤波理论的技术则施加了时空约束,以利于合并多帧信息以生成最佳的心脏运动学估计。使用体内左心室磁共振标签和相衬图像序列从估计的位移场和应变图获得了生理学有意义的结果,这些序列提供了标签-标签和标签-边界位移输入以及中壁瞬时速度信息和边界位移量分别

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