Abstract
The purpose of this study is to investigate how the imposition of personalized, non-invasively measured blood flow rates as boundary conditions (BCs) influences image-based computational hemodynamic studies in the human aorta. We extracted from 4D phase-contrast MRI acquisitions of a healthy human (1) the geometry of the thoracic aorta with supra-aortic arteries and (2) flow rate waveforms at all boundaries. Flow simulations were carried out, and the implications that the imposition of different BC schemes based on the measured flow rates have on wall shear stress (WSS)-based indicators of abnormal flow were analyzed. Our results show that both the flow rate repartition among the multiple outlets of the aorta and the distribution and magnitude of the WSS-based indicators are strongly influenced by the adopted BC strategy. Keeping as reference hemodynamic model the one where the applied BC scheme allowed to obtain a satisfactory agreement between the computed and the measured flow rate waveforms, differences in WSS-based indicators up to 49% were observed when the other BC strategies were applied. In conclusion, we demonstrate that in subject-specific computational hemodynamics models of the human aorta the imposition of BC settings based on non-invasively measured flow rate waveforms influences indicators of abnormal flow to a large extent. Hence, a BCs set-up assuring realistic, subject-specific instantaneous flow rate distribution must be applied when BCs such as flow rates are prescribed.
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Abbreviations
- AAo:
-
Ascending aorta
- BC:
-
Boundary condition
- BCA:
-
Brachiocephalic artery
- CFD:
-
Computational fluid dynamics
- LCCA:
-
Left common carotid artery
- LSA:
-
Left subclavian artery
- OSI:
-
Oscillatory shear index
- PC-MRI:
-
Phase-contrast magnetic resonance imaging
- RRT:
-
Relative residence time
- TAWSS:
-
Time-averaged wall shear stress
- WSS:
-
Wall shear stress
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Gallo, D., De Santis, G., Negri, F. et al. On the Use of In Vivo Measured Flow Rates as Boundary Conditions for Image-Based Hemodynamic Models of the Human Aorta: Implications for Indicators of Abnormal Flow. Ann Biomed Eng 40, 729–741 (2012). https://doi.org/10.1007/s10439-011-0431-1
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DOI: https://doi.org/10.1007/s10439-011-0431-1