Computational methods to reduce uncertainty in the estimation of cardiac conduction properties from electroanatomical recordings.
Computational methods to reduce uncertainty in the estimation of cardiac conduction properties from electroanatomical recordings.
复制标题
减少电解剖记录估计心脏传导特性的不确定性的计算方法。
DOI：
10.1016/j.media.2013.10.006
复制
发表时间：
2014
影响因子：
10.9
通讯作者：
Wallman M
中科院分区：
文献类型：
--
作者：
Wallman M
心脏成像通常用于在治疗前评估心脏组织的特性。通过将结构信息与来自例如电解剖标测系统的电生理数据相结合，可以进一步提炼心脏组织的属性的知识。然而，就像在其他临床模式中一样，电生理数据通常是稀疏和噪声的，这导致估计量的高度不确定性。在这项研究中，我们开发了一种基于贝叶斯推理的方法，结合计算高效的电传播模型来实现两个主要目标：(1)量化从电解剖数据推断的不同组织传导属性的值和相关的不确定性；(2)设计策略以优化所需的测量位置和数量，以最大化信息和减少不确定性。这一方法在一项硅胶研究中得到了验证，该研究使用了从基于人体图像的心室模型获得的模拟数据，包括真实的纤维取向和跨壁疤痕。我们证明，该方法可以同时描述临床相关的电生理传导特性及其在不同噪声水平下的相关不确定性。通过使用开发的方法来研究不确定度如何随增加的测量而降低，然后我们推导出放置电生理测量的优先指数，以便优化收集的数据的信息量。结果表明，与随机分布的测量相比，导出的指数在最小化推断的传导性质的不确定性方面具有明显的优势，在一些调查的设置中，所需的测量次数减少了50%以上。这表明，这项工作中提出的方法提供了重要的一步，以改善使用电解剖映射获得的时空信息的质量。
Cardiac imaging is routinely used to evaluate cardiac tissue properties prior to therapy. By integrating the structural information with electrophysiological data from e.g. electroanatomical mapping systems, knowledge of the properties of the cardiac tissue can be further refined. However, as in other clinical modalities, electrophysiological data are often sparse and noisy, and this results in high levels of uncertainty in the estimated quantities. In this study, we develop a methodology based on Bayesian inference, coupled with a computationally efficient model of electrical propagation to achieve two main aims: (1) to quantify values and associated uncertainty for different tissue conduction properties inferred from electroanatomical data, and (2) to design strategies to optimize the location and number of measurements required to maximize information and reduce uncertainty. The methodology is validated in anin silicostudy performed using simulated data obtained from a human image-based ventricular model, including realistic fibre orientation and a transmural scar. We demonstrate that the method provides a simultaneous description of clinically-relevant electrophysiological conduction properties and their associated uncertainty for various levels of noise. By using the developed methodology to investigate how the uncertainty decreases in response to added measurements, we then derive ana prioriindex for placing electrophysiological measurements in order to optimize the information content of the collected data. Results show that the derived index has a clear benefit in minimizing the uncertainty of inferred conduction properties compared to a random distribution of measurements, reducing the number of required measurements by over 50% in several of the investigated settings. This suggests that the methodology presented in this work provides an important step towards improving the quality of the spatiotemporal information obtained using electroanatomical mapping.