Reconstructing Cardiac Wave Dynamics From Myocardial Motion Data.
Reconstructing Cardiac Wave Dynamics From Myocardial Motion Data.
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DOI：
10.22489/cinc.2020.216
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发表时间：
2020-09
期刊：
影响因子：
--
通讯作者：
Otani NF
中科院分区：
文献类型：
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作者：
Beam CB;Linte CA;Otani NF
存在各种模型来预测心肌组织内的主动应力和膜电位。然而，不存在可靠地测量主动应力的方法，也不存在测量适于体内使用的跨壁膜电位的方法。先前的工作已经设计了一个线性模型，以从组织内的主动应力映射到位移。在组织位移的测量完全精确的情况下，我们能够简单地从测量中求解主动应力。然而，真实的测量过程总是带有一些相关的随机误差，并且在存在这种误差的情况下，我们对这个逆问题的天真解决方案失败了。在这项工作中，我们提出了使用的包围变换卡尔曼滤波器更可靠地解决这个逆问题。这种技术比其他相关的卡尔曼滤波技术更快，同时仍然生成高质量的估计，这改善了我们的天真的解决方案。我们证明，使用在硅片模拟，包围变换卡尔曼滤波器产生的误差，其标准偏差是一个数量级小于最小二乘解决方案。
Various models exist to predict the active stresses and membrane potentials within cardiac muscle tissue. However, there exist no methods to reliably measure active stresses, nor do there exist ways to measure transmural membrane potentials that are suitable for in vivo usage. Prior work has devised a linear model to map from the active stresses within the tissue to displacements. In situations where measurements of tissue displacements are entirely precise, we are able to naively solve for the active stresses from the measurements with ease. However, real measurement processes always carry some associated random error and, in the presence of this error, our naive solution to this inverse problem fails. In this work we propose the use of the Ensemble Transform Kalman Filter to more reliably solve this inverse problem. This technique is faster than other related Kalman Filter techniques while still generating high quality estimates which improve on our naive solution. We demonstrate, using in silico simulations, that the Ensemble Transform Kalman Filter produces errors whose standard deviation is an order of magnitude smaller than the least-squares solution.