Probabilistic noninvasive prediction of wall properties of abdominal aortic aneurysms using Bayesian regression

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• 作者：Jonas Biehler ; Sebastian Kehl…
• 关键词：Abdominal aortic aneurysm ; Bayesian regression ; Wall properties
• 刊名：Biomechanics and Modeling in Mechanobiology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：16
• 期：1
• 页码：45-61
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Theoretical and Applied Mechanics; Biomedical Engineering; Biological and Medical Physics, Biophysics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1617-7940
• 卷排序：16

文摘

Multiple patient-specific parameters, such as wall thickness, wall strength, and constitutive properties, are required for the computational assessment of abdominal aortic aneurysm (AAA) rupture risk. Unfortunately, many of these quantities are not easily accessible and could only be determined by invasive procedures, rendering a computational rupture risk assessment obsolete. This study investigates two different approaches to predict these quantities using regression models in combination with a multitude of noninvasively accessible, explanatory variables. We have gathered a large dataset comprising tensile tests performed with AAA specimens and supplementary patient information based on blood analysis, the patients medical history, and geometric features of the AAAs. Using this unique database, we harness the capability of state-of-the-art Bayesian regression techniques to infer probabilistic models for multiple quantities of interest. After a brief presentation of our experimental results, we show that we can effectively reduce the predictive uncertainty in the assessment of several patient-specific parameters, most importantly in thickness and failure strength of the AAA wall. Thereby, the more elaborate Bayesian regression approach based on Gaussian processes consistently outperforms standard linear regression. Moreover, our study contains a comparison to a previously proposed model for the wall strength.