Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta
Abstract
Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoracic aortic aneurysms (ATAA). To accurately reproduce hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements still makes researchers resort to idealized BCs. In this study we generated and thoroughly characterized a large dataset of synthetic 4D aortic velocity profiles suitable to be used as BCs for CFD simulations. 4D flow MRI scans of 30 subjects with ATAA were processed to extract cross-sectional planes along the ascending aorta, ensuring spatial alignment among all planes and interpolating all velocity fields to a reference configuration. Velocity profiles of the clinical cohort were extensively characterized by computing flow morphology descriptors of both spatial and temporal features. By exploiting principal component analysis (PCA), a statistical shape model (SSM) of 4D aortic velocity profiles was built and a dataset of 437 synthetic cases with realistic properties was generated. Comparison between clinical and synthetic datasets showed that the synthetic data presented similar characteristics as the clinical population in terms of key morphological parameters. The average velocity profile qualitatively resembled a parabolic-shaped profile, but was quantitatively characterized by more complex flow patterns which an idealized profile would not replicate. Statistically significant correlations were found between PCA principal modes of variation and flow descriptors. We built a data-driven generative model of 4D aortic velocity profiles, suitable to be used in computational studies of blood flow. The proposed software system also allows to map any of the generated velocity profiles to the inlet plane of any virtual subject given its coordinate set.
Keywords
Cite
@article{arxiv.2211.00551,
title = {Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta},
author = {Simone Saitta and Ludovica Maga and Chloe Armour and Emiliano Votta and Declan P. O'Regan and M. Yousuf Salmasi and Thanos Athanasiou and Jonathan W. Weinsaft and Xiao Yun Xu and Selene Pirola and Alberto Redaelli},
journal= {arXiv preprint arXiv:2211.00551},
year = {2022}
}
Comments
21 pages, 5 figures, 2 tables To be submitted to "Computer methods and programs in biomedicine" Scripts: https://github.com/saitta-s/flow4D Synthetic velocity profiles: //doi.org/10.5281/zenodo.7251987