2015
DOI: 10.1002/cnm.2737
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Uncertainty quantification in virtual surgery hemodynamics predictions for single ventricle palliation

Abstract: The adoption of simulation tools to predict surgical outcomes is increasingly leading to questions about the variability of these predictions in the presence of uncertainty associated with the input clinical data. In the present study, we propose a methodology for full propagation of uncertainty from clinical data to model results that, unlike deterministic simulation, enables estimation of the confidence associated with model predictions. We illustrate this problem in a virtual stage II single ventricle palli… Show more

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Cited by 62 publications
(69 citation statements)
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“…In addition, modules for optimization, uncertainty quantification, and parameter estimation to match clinical data are under development. 53,64 Lastly, new algorithms to convert a discrete model (typical output of direct 3D segmentation methods) to an analytic model (CAD standard) are being developed, and will enable users to import/export models that are editable in most CAD frameworks.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, modules for optimization, uncertainty quantification, and parameter estimation to match clinical data are under development. 53,64 Lastly, new algorithms to convert a discrete model (typical output of direct 3D segmentation methods) to an analytic model (CAD standard) are being developed, and will enable users to import/export models that are editable in most CAD frameworks.…”
Section: Discussionmentioning
confidence: 99%
“…Recent work has extended these approaches to propagate uncertainty directly from the clinical data to the simulation outputs. [34]…”
Section: Advances In Modeling Methodsmentioning
confidence: 99%
“…The subject is part of the data-set analyzed in Morbiducci et al (2011a). models: reconstructed vessel geometry (Sankaran & Marsden 2011;Sankaran et al 2015Sankaran et al , 2016, input and output BCs (Sankaran & Marsden 2011;Morbiducci et al 2013;Tiago et al 2014;Valen-Sendstad et al 2015;Schiavazzi et al 2016;Tran et al 2017), vessel distensibility and motion (Jin et al 2003;Zhao et al 2000;Eck et al 2016;Javadzadegan et al 2016) and rheological properties of blood (Lee & Steinman 2007;Morbiducci et al 2011b). In a recent study, the Authors reported a numerical experiment in which different possible strategies of applying PC-MRI measured flow data as BCs in computational hemodynamic models of healthy human aorta were implemented (Morbiducci et al 2013).…”
Section: Pc-mri Datamentioning
confidence: 99%