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Estimating myocardial motion by 4D image warping

机译:通过4D图像扭曲估算心肌运动

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摘要

A method for spatio-temporally smooth and consistent estimation of cardiac motion from MR cine sequences is proposed. Myocardial motion is estimated within a four-dimensional (4D) registration framework, in which all three-dimensional (3D) images obtained at different cardiac phases are simultaneously registered. This facilitates spatio-temporally consistent estimation of motion as opposed to other registration-based algorithms which estimate the motion by sequentially registering one frame to another. To facilitate image matching, an attribute vector (AV) is constructed for each point in the image, and is intended to serve as a "morphological signature" of that point. The AV includes intensity, boundary, and geometric moment invariants (GMIs). Hierarchical registration of two image sequences is achieved by using the most distinctive points for initial registration of two sequences and gradually adding less-distinctive points to refine the registration. Experimental results on real data demonstrate good performance of the proposed method for cardiac image registration and motion estimation. The motion estimation is validated via comparisons with motion estimates obtained from MR images with myocardial tagging.
机译:提出了一种从MR电影序列中进行时空平滑且一致的心脏运动估计的方法。在一个四维(4D)配准框架中估计心肌运动,在该框架中,同时记录在不同心脏阶段获得的所有三维(3D)图像。相对于其他基于配准的算法,该算法通过顺序地将一个帧注册到另一帧来估计运动,这有助于对运动进行时空一致的估计。为促进图像匹配,为图像中的每个点构造了一个属性矢量(AV),旨在用作该点的“形态特征”。 AV包括强度,边界和几何矩不变量(GMI)。通过使用最独特的点进行两个序列的初始配准并逐渐添加区别较小的点来优化配准,可以实现两个图像序列的分层配准。在真实数据上的实验结果证明了所提出的用于心脏图像配准和运动估计的方法的良好性能。通过与从带有心肌标签的MR图像获得的运动估计值进行比较来验证运动估计值。

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