A Metamodeling Approach for Instant Severity Assessment and Uncertainty Quantification of Iliac Artery Stenoses

Stefan Heinen*, Kujtim Gashi, Daniel van den Heuvel, Jean-Paul de Vries, Frans van de Vosse, Tammo Delhaas, Wouter Huberts

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Two-dimensional (2D) or three-dimensional (3D) models of blood flow in stenosed arteries can be used to patient-specifically predict outcome metrics, thereby supporting the physicians in decision making processes. However, these models are time consuming which limits the feasibility of output uncertainty quantification (UQ). Accurate surrogates (metamodels) might be the solution. In this study, we aim to demonstrate the feasibility of a generalized polynomial chaos expansion-based metamodel to predict a clinically relevant output metric and to quantify the output uncertainty. As an example, a metamodel was constructed from a recently developed 2D model that was shown to be able to estimate translesional pressure drops in iliac artery stenoses (-0.9 +/- 12.7 mmHg, R-2 = 0.81). The metamodel was constructed from a virtual database using the adaptive generalized polynomial chaos expansion (agPCE) method. The constructed metamodel was then applied to 25 stenosed iliac arteries to predict the patient-specific pressure drop and to perform UQ. Comparing predicted pressure drops of the metamodel and in vivo measured pressure drops, the mean bias (-0.2 +/- 13.7 mmHg) and the coefficient of determination (R-2 = 0.80) were as good as of the original 2D computational fluid dynamics (CFD) model. UQ results of the 2D and metamodel were comparable. Estimation of the uncertainty interval using the original 2D model took 14 days, whereas the result of the metamodel was instantly available. In conclusion, it is feasible to quantify the uncertainty of the output metric and perform sensitivity analysis (SA) instantly using a metamodel. Future studies should investigate the possibility to construct a metamodel of more complex problems.

Original languageEnglish
Article number011010
Number of pages8
JournalJournal of Biomechanical Engineering-Transactions of the Asme
Issue number1
Early online date1 Aug 2019
Publication statusPublished - 1 Jan 2020


  • metamodeling
  • computer-aided diagnosis
  • computational fluid dynamics
  • uncertainty quantification
  • peripheral arterial disease

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