Dept. of Electr. Eng., Auburn Univ., AL.
IEEE Trans Med Imaging. 1995;14(4):625-35. doi: 10.1109/42.476104.
Magnetic resonance (MR) tagging has shown great potential for noninvasive measurement of the motion of a beating heart. In MR tagged images, the heart appears with a spatially encoded pattern that moves with the tissue. The position of the tag pattern in each frame of the image sequence can be used to obtain a measurement of the 3-D displacement field of the myocardium. The measurements are sparse, however, and interpolation is required to reconstruct a dense displacement field from which measures of local contractile performance such as strain can be computed. Here, the authors propose a method for estimating a dense displacement field from sparse displacement measurements. Their approach is based on a multidimensional stochastic model for the smoothness and divergence of the displacement field and the Fisher estimation framework. The main feature of this method is that both the displacement field model and the resulting estimate equation are defined only on the irregular domain of the myocardium. The authors' methods are validated on both simulated and in vivo heart data.
磁共振（MR）标记在无创测量跳动心脏的运动方面显示出巨大的潜力。在 MR 标记图像中，心脏呈现出随组织运动的空间编码模式。图像序列中每一帧的标记模式的位置可用于获得心肌的 3-D 位移场的测量值。然而，这些测量值是稀疏的，需要进行插值才能从密集的位移场重建中计算局部收缩性能（如应变）的测量值。在这里，作者提出了一种从稀疏位移测量值估计密集位移场的方法。他们的方法基于位移场的平滑度和散度的多维随机模型以及 Fisher 估计框架。该方法的主要特点是，位移场模型和由此产生的估计方程都仅在心肌的不规则区域上定义。作者的方法在模拟和体内心脏数据上都得到了验证。
