Examining the Impact of Prior Models in Transmural Electrophysiological Imaging: A Hierarchical Multiple-Model Bayesian Approach.
Examining the Impact of Prior Models in Transmural Electrophysiological Imaging: A Hierarchical Multiple-Model Bayesian Approach.
复制标题
检查先前模型在跨壁电生理成像中的影响：一种分层多模型贝叶斯方法。
DOI：
10.1109/tmi.2015.2464315
复制
发表时间：
2016-01
影响因子：
10.6
通讯作者：
Wang L
中科院分区：
文献类型：
--
作者：
Rahimi A;Sapp J;Xu J;Bajorski P;Horacek M;Wang L
无创心脏电生理（EP）成像旨在根据体表心电图（ECG）数据以数学方式重建心脏源的时空动态。这种不适定问题通常通过固定约束模型进行正则化。然而，固定模型方法强制源分布遵循预先假定的结构，该结构并不总是与实际源的变化的时空分布相匹配。为了了解模型与数据的关系并检查先前模型的影响，我们提出了一种用于体积心脏 EP 成像的多模型方法，其中包含多个先前模型，并通过可用的 ECG 数据自动挑选。多个模型被合并为源的 Lp 范数先验，其中 p 是具有先验均匀分布的未知超参数。为了检查不同的测量数据如何支持不同的模型组合，使用马尔可夫链蒙特卡罗 (MCMC) 技术计算心脏源和超参数 p 的后验分布。与固定模型先验（使用拉普拉斯和高斯先验）相比，多模型先验的重要性在两组合成和真实数据实验中进行了评估。结果表明，在重建不同大小和结构的源时，由心电图数据确定的模型后验组合（p 的后验分布）存在显着差异。虽然使用固定模型最适合先验假设适合实际源结构的情况，但使用自动自适应模型集可能能够更好地解决模型数据不匹配问题，并在重建具有不同属性的源时提供一致的性能。
Noninvasive cardiac electrophysiological (EP) imaging aims to mathematically reconstruct the spatiotemporal dynamics of cardiac sources from body-surface electrocardiographic (ECG) data. This ill-posed problem is often regularized by a fixed constraining model. However, a fixed-model approach enforces the source distribution to follow a pre-assumed structure that does not always match the varying spatiotemporal distribution of actual sources. To understand the model-data relation and examine the impact of prior models, we present a multiple-model approach for volumetric cardiac EP imaging where multiple prior models are included and automatically picked by the available ECG data. Multiple models are incorporated as an Lp-norm prior for sources, where p is an unknown hyperparameter with a prior uniform distribution. To examine how different combinations of models may be favored by different measurement data, the posterior distribution of cardiac sources and hyperparameter p is calculated using a Markov Chain Monte Carlo (MCMC) technique. The importance of multiple-model prior was assessed in two sets of synthetic and real-data experiments, compared to fixed-model priors (using Laplace and Gaussian priors). The results showed that the posterior combination of models (the posterior distribution of p) as determined by the ECG data differed substantially when reconstructing sources with different sizes and structures. While the use of fixed models is best suited in situations where the prior assumption fits the actual source structures, the use of an automatically adaptive set of models may have the ability to better address model-data mismatch and to provide consistent performance in reconstructing sources with different properties.