Calibration of parameters for cardiovascular models with application to arterial growth
Calibration of parameters for cardiovascular models with application to arterial growth
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DOI：
10.1002/cnm.2822
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发表时间：
2017-05-01
影响因子：
2.1
通讯作者：
Gee, Michael W.
中科院分区：
文献类型：
--
作者：
Kehl, Sebastian;Gee, Michael W.
我们提出了一个计算框架，用于校准描述心血管模型的参数，重点是腹主动脉瘤（AAA）生长的应用。这种病理的增长率被认为是风险管理中的关键参数，也是评估监测间隔的重要指标。描述AAAs生长的参数不能直接测量，需要根据医学成像技术提供的可用数据进行估计。通常应用于参数识别的标准工作流程中以提取图像编码信息的配准过程是显著系统误差的来源。表面电流的概念提供了有效避免这种误差来源的手段，建立了一个数学框架，比较表面信息，直接从图像数据。通过利用这一概念，它是可能的反估计生长参数，使用复杂的数值模型的AAAs从测量可作为表面信息。在这项工作中，我们提出了一个框架，以获得控制动脉组织生长的参数的空间分布，我们展示了如何使用表面电流可以显着提高结果。我们进一步提出了应用于患者的具体后续数据，从而在空间地图的体积增长率，使第一次，预测进一步的AAA扩展。
We present a computational framework for the calibration of parameters describing cardiovascular models with a focus on the application of growth of abdominal aortic aneurysms (AAA). The growth rate in this sort of pathology is considered a critical parameter in the risk management and is an essential indicator for the assessment of surveillance intervals. Parameters describing growth of AAAs are not measurable directly and need to be estimated from available data often given by medical imaging technologies. Registration procedures often applied in standard workflows of parameter identification to extract the image encoded information are a source of significant systematic error. The concept of surface currents provides means to effectively avoid this source of errors by establishing a mathematical framework to compare surface information, directly accessible from image data. By utilizing this concept it is possible to inversely estimate growth parameters using sophisticated numerical models of AAAs from measurements available as surface information. In this work we present a framework to obtain spatial distributions of parameters governing growth of arterial tissue, and we show how the use of surface currents can significantly improve the results. We further present the application to patient specific follow-up data resulting in a spatial map of volumetric growth rates enabling, for the first time, prediction of further AAA expansion.