Alessandro Boccadifuoco1, Alessandro Mariotti2, Katia Capellini3, Simona Celi3, Maria Vittoria Salvetti4. 1. Institute of Life Sciences, Scuola Superiore Sant'Anna, Pisa, Italy. 2. Dipartimento di Ingegneria Civile e Industriale, Università di Pisa, Pisa, Italy. alessandro.mariotti@for.unipi.it. 3. BioCardioLab, Bioengineering Unit, Fondazione Toscana G. Monasterio, Heart Hospital, Massa, Italy. 4. Dipartimento di Ingegneria Civile e Industriale, Università di Pisa, Pisa, Italy.
Abstract
PURPOSE: Computational fluid dynamics (CFD) and 4D-flow magnetic resonance imaging (MRI) are synergically used for the simulation and the analysis of the flow in a patient-specific geometry of a healthy thoracic aorta. METHODS: CFD simulations are carried out through the open-source code SimVascular. The MRI data are used, first, to provide patient-specific boundary conditions. In particular, the experimentally acquired flow rate waveform is imposed at the inlet, while at the outlets the RCR parameters of the Windkessel model are tuned in order to match the experimentally measured fractions of flow rate exiting each domain outlet during an entire cardiac cycle. Then, the MRI data are used to validate the results of the hemodynamic simulations. As expected, with a rigid-wall model the computed flow rate waveforms at the outlets do not show the time lag respect to the inlet waveform conversely found in MRI data. We therefore evaluate the effect of wall compliance by using a linear elastic model with homogeneous and isotropic properties and changing the value of the Young's modulus. A stochastic analysis based on the polynomial chaos approach is adopted, which allows continuous response surfaces to be obtained in the parameter space starting from a few deterministic simulations. RESULTS: The flow rate waveform can be accurately reproduced by the compliant simulations in the ascending aorta; on the other hand, in the aortic arch and in the descending aorta, the experimental time delay can be matched with low values of the Young's modulus, close to the average value estimated from experiments. However, by decreasing the Young's modulus the underestimation of the peak flow rate becomes more significant. As for the velocity maps, we found a generally good qualitative agreement of simulations with MRI data. The main difference is that the simulations overestimate the extent of reverse flow regions or predict reverse flow when it is absent in the experimental data. Finally, a significant sensitivity to wall compliance of instantaneous shear stresses during large part of the cardiac cycle period is observed; the variability of the time-averaged wall shear stresses remains however very low. CONCLUSIONS: In summary, a successful integration of hemodynamic simulations and of MRI data for a patient-specific simulation has been shown. The wall compliance seems to have a significant impact on the numerical predictions; a larger wall elasticity generally improves the agreement with experimental data.
PURPOSE: Computational fluid dynamics (CFD) and 4D-flow magnetic resonance imaging (MRI) are synergically used for the simulation and the analysis of the flow in a patient-specific geometry of a healthy thoracic aorta. METHODS:CFD simulations are carried out through the open-source code SimVascular. The MRI data are used, first, to provide patient-specific boundary conditions. In particular, the experimentally acquired flow rate waveform is imposed at the inlet, while at the outlets the RCR parameters of the Windkessel model are tuned in order to match the experimentally measured fractions of flow rate exiting each domain outlet during an entire cardiac cycle. Then, the MRI data are used to validate the results of the hemodynamic simulations. As expected, with a rigid-wall model the computed flow rate waveforms at the outlets do not show the time lag respect to the inlet waveform conversely found in MRI data. We therefore evaluate the effect of wall compliance by using a linear elastic model with homogeneous and isotropic properties and changing the value of the Young's modulus. A stochastic analysis based on the polynomial chaos approach is adopted, which allows continuous response surfaces to be obtained in the parameter space starting from a few deterministic simulations. RESULTS: The flow rate waveform can be accurately reproduced by the compliant simulations in the ascending aorta; on the other hand, in the aortic arch and in the descending aorta, the experimental time delay can be matched with low values of the Young's modulus, close to the average value estimated from experiments. However, by decreasing the Young's modulus the underestimation of the peak flow rate becomes more significant. As for the velocity maps, we found a generally good qualitative agreement of simulations with MRI data. The main difference is that the simulations overestimate the extent of reverse flow regions or predict reverse flow when it is absent in the experimental data. Finally, a significant sensitivity to wall compliance of instantaneous shear stresses during large part of the cardiac cycle period is observed; the variability of the time-averaged wall shear stresses remains however very low. CONCLUSIONS: In summary, a successful integration of hemodynamic simulations and of MRI data for a patient-specific simulation has been shown. The wall compliance seems to have a significant impact on the numerical predictions; a larger wall elasticity generally improves the agreement with experimental data.
Authors: Elisa Fevola; Francesco Ballarin; Laura Jiménez-Juan; Stephen Fremes; Stefano Grivet-Talocia; Gianluigi Rozza; Piero Triverio Journal: Int J Numer Method Biomed Eng Date: 2021-08-15 Impact factor: 2.648
Authors: Federica Cosentino; Giuseppe M Raffa; Giovanni Gentile; Valentina Agnese; Diego Bellavia; Michele Pilato; Salvatore Pasta Journal: J Pers Med Date: 2020-04-22