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Session: Vascular I
Session starts: Thursday 26 January, 14:30
Presentation starts: 14:45
Room: Room 559

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Selene Pirola (TU Delft)

Abstract:
Computational fluid dynamics (CFD) has emerged as a powerful tool to investigate development and growth of aortic aneurysms. Our previous work1 showed that inlet boundary conditions (IBC) are crucial to accurately reproduce blood flow features in the ascending aorta. However, the availability of in vivo measurements to be used as IBC is limited. This hinders progress of research on ascending aortic disease. With this work2, we aim to address this issue by proposing a data-driven generative model of 4D aortic velocity profiles suitable for use in CFD modelling of the ascending aorta. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic inlet velocity profiles was developed starting from 4D flow magnetic resonance imaging scans of 30 subjects with ascending thoracic aortic aneurysm. Using the SSM, a dataset of 500 synthetic cases was generated. Velocity profiles from both the clinical and synthetic cohorts were extensively characterized by computing flow morphology descriptors (e.g., flow jet angle - FJA) of both spatial and temporal features. The synthetic dataset was then further refined by excluding generated profiles which presented flow descriptors outside the physiological range observed in the clinical cohort. This selection resulted in the acceptance of 437 synthetic profiles with realistic properties. T-tests and Mann–Whitney U test confirmed that no statistically significant differences existed between the two cohorts. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors in the synthetic cohort: e.g., mode 1 strongly correlated (r=0.94, p<0.0001) with the spatial heterogeneity of the velocity magnitude (quantified by the flow dispersion index3). The average velocity profile obtained by the conducted PCA qualitatively resembled a parabolic-shaped profile but was quantitatively characterized by more complex features – such as 13° FJA at peak systole and non-null in-plane velocity. This further supports the need for more realistic IBC for ascending thoracic aorta simulations. Therefore, to allow for the computational research community to benefit from more realistic IBCs, we have released2 the 437 generated synthetic profiles. We believe that the present work will allow to replace the common practice of prescribing idealized IBCs in numerical simulations of blood flow with more realistic conditions. 1Pirola S, et al. APL Bioeng. 2018;2(2):026101. 2Saitta S, et al. arXiv 2022, https://arxiv.org/abs/2211.00551. 3Youssefi P, et al. J Biomech Eng. 2018;140(1):011002.