Uncertainty propagation of phase contrast-MRI derived inlet boundary conditions in computational hemodynamics models of thoracic aorta

Bozzi, Silvia; Morbiducci, Umberto; Gallo, Diego; Ponzini, Raffaele; Rizzo, Giovanna; Bignardi, Cristina; Passoni, Giuseppe

doi:10.1080/10255842.2017.1334770

Cited by 40 publications

(21 citation statements)

References 38 publications

(45 reference statements)

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“…Then, grounded on the noisy nature of PC‐MRI acquisition, we perform a sensitivity analysis following Bozzi et al to assess the impact of uncertainties in our parameter estimation.…”

Section: Resultsmentioning

confidence: 99%

“…Grounded on this description and following the approach by Bozzi et al, we generated a set of 1000 “noisy” flow waves drawing independent samples from a Gaussian distribution. The mean value was our original PC‐MRI flow information whereas the standard deviation was σ = μ /16.…”

Section: Resultsmentioning

confidence: 99%

“…In Section 1, we have stated many of the weaknesses of imposing flow data as velocity boundary conditions. Alongside with the development of our tuning strategy, we analyzed how would uncertainty of the PC‐MRI acquisition impact the CFD outcomes as described by Bozzi et al The reduction of the SNR by one order of magnitude constitutes yet another strength of using lumped models through constrained optimization: Even if the acquisition is noisy, the impact in the solution of the NSE would be negligible.…”

Section: Discussionmentioning

confidence: 99%

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Patient‐specific CFD modelling in the thoracic aorta with PC‐MRI–based boundary conditions: A least‐square three‐element Windkessel approach

Romarowski

Lefieux

Morganti

et al. 2018

Numer Methods Biomed Eng

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The increasing use of computational fluid dynamics for simulating blood flow in clinics demands the identification of appropriate patient-specific boundary conditions for the customization of the mathematical models. These conditions should ideally be retrieved from measurements. However, finite resolution of devices as well as other practical/ethical reasons prevent the construction of complete data sets necessary to make the mathematical problems well posed. Available data need to be completed by modelling assumptions, whose impact on the final solution has to be carefully addressed. Focusing on aortic vascular districts and related pathologies, we present here a method for efficiently and robustly prescribing phase contrast MRI-based patient-specific data as boundary conditions at the domain of interest. In particular, for the outlets, the basic idea is to obtain pressure conditions from an appropriate elaboration of available flow rates on the basis of a 3D/0D dimensionally heterogeneous modelling. The key point is that the parameters are obtained by a constrained optimization procedure. The rationale is that pressure conditions have a reduced impact on the numerical solution compared with velocity conditions, yielding a simulation framework less exposed to noise and inconsistency of the data, as well as to the arbitrariness of the underlying modelling assumptions. Numerical results confirm the reliability of the approach in comparison with other patient-specific approaches adopted in the literature.

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“…Then, grounded on the noisy nature of PC‐MRI acquisition, we perform a sensitivity analysis following Bozzi et al to assess the impact of uncertainties in our parameter estimation.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Patient‐specific CFD modelling in the thoracic aorta with PC‐MRI–based boundary conditions: A least‐square three‐element Windkessel approach

Romarowski

Lefieux

Morganti

et al. 2018

Numer Methods Biomed Eng

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“…In such way, (14) only requires the LF data and a finite number of the first k + 1 pre-selected HF samples, if c 1 and c 2 are determined properly.…”

Section: An Empirical Error Bound Estimationmentioning

confidence: 99%

“…Uncertainty quantification (UQ) and sensitivity analysis (SA) of cardiovascular modeling have been gaining increasing attention in the past decade. There are numerous literature on examining the influence of variations in inflow/outflow boundary conditions [6][7][8][9][10][11][12][13][14][15][16][17][18], segmented vascular geometry [19][20][21][22][23][24][25][26][27][28], and mechanical properties of blood flow or vessel walls [29][30][31][32][33] on the simulated hemodynamics. However, the majority of these works focused on investigating the sensitivity of the model to its input factors using ad hoc perturbation analysis but have yet to rigorously characterize and quantify the uncertainty distributions.…”

Section: Introductionmentioning

confidence: 99%

A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations

Gao

Zhu

Wang

2020

Computer Methods in Applied Mechanics and Engineering

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Image-based computational fluid dynamics (CFD) modeling enables derivation of hemodynamic information (e.g., flow field, wall shear stress, and pressure distribution), which has become a paradigm in cardiovascular research and healthcare. Nonetheless, the predictive accuracy largely depends on precisely specified boundary conditions and model parameters, which, however, are usually uncertain (or unknown) in most patient-specific cases. Quantifying the uncertainties in model predictions due to input randomness can provide predictive confidence and is critical to promote the transition of CFD modeling in clinical applications.In the meantime, forward propagation of input uncertainties often involves numerous expensive CFD simulations, which is computationally prohibitive in most practical scenarios. This paper presents an efficient bi-fidelity surrogate modeling framework for uncertainty quantification (UQ) in cardiovascular simulations, by leveraging the accuracy of high-fidelity models and efficiency of low-fidelity models. Contrary to most data-fit surrogate models with several scalar quantities of interest, this work aims to provide high-resolution, full-field predictions (e.g., velocity and pressure fields). Moreover, a novel empirical error bound estimation approach is introduced to evaluate the performance of the surrogate a priori.The proposed framework is tested on a number of vascular flows with both standardized and patient-specific vessel geometries, and different combinations of high-and low-fidelity models are investigated. The results show that the bi-fidelity approach can achieve high * Corresponding authors. jwang33@nd.edu (J.-X. Wang), xueyu-zhu@uiowa.edu (X. Zhu) predictive accuracy with a significant reduction of computational cost, exhibiting its merit and effectiveness. Particularly, the uncertainties from a high-dimensional input space can be accurately propagated to clinically relevant quantities of interest (e.g., wall shear stress) in the patient-specific case using only a limited number of high-fidelity simulations, suggesting a good potential in practical clinical applications.

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A Bayesian approach to blood rheological uncertainties in aortic hemodynamics

Ranftl

Müller

Windberger

et al. 2022

Numer Methods Biomed Eng

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Computational hemodynamics has received increasing attention recently.Patient-specific simulations require questionable model assumptions, for example, for geometry, boundary conditions, and material parameters. Consequently, the credibility of these simulations is much doubted, and rightly so. Yet, the matter may be addressed by a rigorous uncertainty quantification. In this contribution, we investigated the impact of blood rheological models on wall shear stress uncertainties in aortic hemodynamics obtained in numerical simulations. Based on shear-rheometric experiments, we compare the non-Newtonian Carreau model to a simple Newtonian model and a Reynolds number-equivalent Newtonian model. Bayesian Probability Theory treats uncertainties consistently and allows to include elusive assumptions such as the comparability of flow regimes. We overcome the prohibitively high computational cost for the simulation with a surrogate model, and account for the uncertainties of the surrogate model itself, too. We have two main findings: (1) The Newtonian models mostly underestimate the uncertainties as compared to the non-Newtonian model.(2) The wall shear stresses of specific persons cannot be distinguished due to largely overlapping uncertainty bands, implying that a more precise determination of person-specific blood rheological properties is necessary for personspecific simulations. While we refrain from a general recommendation for one rheological model, we have quantified the error of the uncertainty quantification associated with these modeling choices.

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Uncertainty propagation of phase contrast-MRI derived inlet boundary conditions in computational hemodynamics models of thoracic aorta

Cited by 40 publications

References 38 publications

Patient‐specific CFD modelling in the thoracic aorta with PC‐MRI–based boundary conditions: A least‐square three‐element Windkessel approach

Patient‐specific CFD modelling in the thoracic aorta with PC‐MRI–based boundary conditions: A least‐square three‐element Windkessel approach

A bi-fidelity surrogate modeling approach for uncertainty propagation in three-dimensional hemodynamic simulations

A Bayesian approach to blood rheological uncertainties in aortic hemodynamics

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