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BY-NC-ND 4.0 license Open Access Published by De Gruyter September 22, 2018

Monte-Carlo parameter variation study of cardiovascular pathologies to quantify parameter specific signal uncertainty

  • Stefan Krickl EMAIL logo and Stefan Bernhard


Cardiovascular diseases are the leading cause of death worldwide. Early detection of abnormal vascular morphologies like aneurysms in the abdominal (abdominal aortic aneurysm, AAA) or thoracic aorta (thoracic aortic aneurysm, TAA) are essential to prevent fatal events. The aim of this study is the development of a patient-specific simulation model to obtain statistical information about ab- /normal pressure-flow conditions to improve the basic understanding and methods for the early detection of diseases. For this purpose, the numerical cardiovascular modeling tool SISCA was used, to generate a series of simulations by Monte- Carlo parameter variation. The considered variational scenario was built upon a control group of normal patients, deriving two pathological conditions for AAA and TAA with different severity and location. Therefore, the nominal diameters were enlarged between 200 % and 500 %, while the length of the aneurysms were modified within a range of 30 and 90 mm. Within each statistical set the convergence was tested by the bootstrap method ensuring that within a set of 3500 runs a 2 % deviation error of the mean value of the blood pressure was obtained compared to a set of 10000 runs.The parameter variation method allows the generation of disease specific data in the context of physiological/clinical findings and consequently the disease specific quantification of signal uncertainties and variances.

Published Online: 2018-09-22
Published in Print: 2018-09-01

© 2018 the author(s), published by Walter de Gruyter Berlin/Boston

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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