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.Comment: 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.725198