Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling.
Sensitivity of Noninvasive Cardiac Electrophysiological Imaging to Variations in Personalized Anatomical Modeling.
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DOI：
10.1109/tbme.2015.2395387
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发表时间：
2015-06
期刊：
影响因子：
--
通讯作者：
Linwei Wang
中科院分区：
文献类型：
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作者：
Rahimi A;Linwei Wang
无创心脏电生理（EP）成像技术依赖于从个体受试者的高质量断层成像图像中获得的解剖细节心脏-躯干模型。然而，解剖建模涉及到导致EP成像结果未解决的不确定性的变化，给这些方法在临床实践中的稳健性带来了问题。在这项研究中，我们设计了一个系统的统计方法来评估电位成像方法对个性化解剖模型变化的敏感性。我们首先通过统计形状建模的新应用量化个性化解剖模型的变化。考虑到个性化解剖模型变化的统计分布，我们随后采用unscented变换来确定EP成像输出对输入个性化解剖模型变化的敏感性。我们使用两种现有的心电成像方法来测试我们提出的方法的可行性：心外膜心电图成像和跨壁电生理成像。幻像和真实数据实验都表明，个性化解剖模型的变化对电位成像结果的影响可以忽略不计。本研究验证了电位成像方法对个性化解剖建模误差的鲁棒性，并为今后临床实践中简化解剖建模过程提供了可能性。本研究提出了一种系统的统计方法来量化解剖模型的变化，并评估其对脑电图成像的影响，可以扩展到寻找个性化解剖模型质量与脑电图成像准确性之间的平衡，从而提高脑电图成像的临床可行性。
Noninvasive cardiac electrophysiological (EP) imaging techniques rely on anatomically-detailed heart-torso models derived from high-quality tomographic images of individual subjects. However, anatomical modeling involves variations that lead to unresolved uncertainties in the outcome of EP imaging, bringing questions to the robustness of these methods in clinical practice. In this study, we design a systematic statistical approach to assess the sensitivity of EP imaging methods to the variations in personalized anatomical modeling. We first quantify the variations in personalized anatomical models by a novel application of statistical shape modeling. Given the statistical distribution of the variation in personalized anatomical models, we then employ unscented transform to determine the sensitivity of EP imaging outputs to the variation in input personalized anatomical modeling. We test the feasibility of our proposed approach using two of the existing EP imaging methods: epicardial-based electrocardiographic imaging and transmural electrophysiological imaging. Both phantom and real-data experiments show that variations in personalized anatomical models have negligible impact on the outcome of EP imaging. This study verifies the robustness of EP imaging methods to the errors in personalized anatomical modeling and suggests the possibility to simplify the process of anatomical modeling in future clinical practice. This study proposes a systematic statistical approach to quantify anatomical modeling variations and assess their impact on EP imaging, which can be extended to find a balance between the quality of personalized anatomical models and the accuracy of EP imaging that may improve the clinical feasibility of EP imaging.