Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta

Saitta, Simone (Politecnico di Milano)
Maga, L.M. (Politecnico di Milano; Imperial College London)
Armour, Chloe (Imperial College London)
Votta, Emiliano (Politecnico di Milano)
O'Regan, Declan P. (Imperial College London)
Salmasi, M. Yousuf (Imperial College London)
Athanasiou, Thanos (Imperial College London)
Weinsaft, Jonathan W. (Weill Cornell Medical College)
Xu, Xiao Yun (Imperial College London)
Pirola, S. (TU Delft Medical Instruments & Bio-Inspired Technology; Imperial College London)
Redaelli, Alberto (Politecnico di Milano)

2023

Background and Objective: Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoratic aortic aneurysms (ATAA). To accurately reproduce in vivo 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. The aim of this study was to generate and thoroughly characterize a large dataset of synthetic 4D aortic velocity profiles sampled on a 2D cross-section along the ascending aorta with features similar to clinical cohorts of patients with ATAA. Methods: Time-resolved 3D phase contrast magnetic resonance (4D flow MRI) scans of 30 subjects with ATAA were processed through in-house code to extract anatomically consistent 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. Results: 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. Conclusions: We built a data-driven generative model of 4D aortic inlet 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.

4D Flow magnetic resonance imaging
Aortic velocity profile
Ascending aortic aneurysm
Inflow boundary conditions
Statistical shape modeling

http://resolver.tudelft.nl/uuid:a4dfb081-4be3-4915-a733-af163ca7d97b

https://doi.org/10.1016/j.cmpb.2023.107468

0169-2607

Computer Methods and Programs in Biomedicine, 233

Institutional Repository

journal article

© 2023 Simone Saitta, L.M. Maga, Chloe Armour, Emiliano Votta, Declan P. O'Regan, M. Yousuf Salmasi, Thanos Athanasiou, Jonathan W. Weinsaft, Xiao Yun Xu, S. Pirola, Alberto Redaelli