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International Journal for Uncertainty Quantification

年間 6 号発行

ISSN 印刷: 2152-5080

ISSN オンライン: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

MULTIFIDELITY ESTIMATORS FOR CORONARY CIRCULATION MODELS UNDER CLINICALLY INFORMED DATA UNCERTAINTY

巻 10, 発行 5, 2020, pp. 449-466
DOI: 10.1615/Int.J.UncertaintyQuantification.2020033068
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要約

Numerical models are increasingly used for noninvasive diagnosis and treatment planning in coronary artery disease, where service-based technologies have proven successful in identifying hemodynamically significant and hence potentially dangerous vascular anomalies. Despite recent progress towards clinical adoption, many results in the field are still based on a deterministic characterization of blood flow, with no quantitative assessment of the variability of simulation outputs due to uncertainty from multiple sources. In this study, we focus on parameters that are essential to construct accurate patient-specific representations of the coronary circulation, such as aortic pressure waveform and intramyocardial pressure, and quantify how their uncertainty affects clinically relevant model outputs. We construct a deformable model of the left coronary artery subject to a prescribed inlet pressure and with open-loop outlet boundary conditions, treating fluid-structure interaction through an arbitrary-Lagrangian-Eulerian framework. Random input uncertainty is estimated directly from repeated clinical measurements from intracoronary catheterization and complemented by literature data.We also achieve significant computational cost reductions in uncertainty propagation thanks to multifidelity Monte Carlo estimators of the outputs of interest, leveraging the ability to generate, at practically no cost, one- and zero-dimensional low-fidelity representations of left coronary artery flow, with appropriate boundary conditions. The results demonstrate how the use of multifidelity control variate estimators leads to significant reductions in variance and accuracy improvements with respect to traditional Monte Carlo. In particular, the combination of three-dimensional hemodynamics simulations and zero-dimensional lumped parameter network models produces the best results, with only a negligible (less than 1%) computational overhead.

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によって引用された
  1. Seo Jongmin, Schiavazzi Daniele E., Kahn Andrew M., Marsden Alison L., The effects of clinically‐derived parametric data uncertainty in patient‐specific coronary simulations with deformable walls, International Journal for Numerical Methods in Biomedical Engineering, 36, 8, 2020. Crossref

  2. Vardhan M., Randles A., Application of physics-based flow models in cardiovascular medicine: Current practices and challenges, Biophysics Reviews, 2, 1, 2021. Crossref

  3. Pfaller Martin R., Pham Jonathan, Wilson Nathan M., Parker David W., Marsden Alison L., On the Periodicity of Cardiovascular Fluid Dynamics Simulations, Annals of Biomedical Engineering, 49, 12, 2021. Crossref

  4. Pfaller Martin R., Pham Jonathan, Verma Aekaansh, Pegolotti Luca, Wilson Nathan M., Parker David W., Yang Weiguang, Marsden Alison L., Automated generation of 0D and 1D reduced‐order models of patient‐specific blood flow , International Journal for Numerical Methods in Biomedical Engineering, 38, 10, 2022. Crossref

  5. Zhu Chi, Vedula Vijay, Parker Dave, Wilson Nathan, Shadden Shawn, Marsden Alison, svFSI: A Multiphysics Package for Integrated Cardiac Modeling, Journal of Open Source Software, 7, 78, 2022. Crossref

  6. Du Pan, Wang Jian-Xun, Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC, Journal of Biomechanical Engineering, 144, 12, 2022. Crossref

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