Imperial College London
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Professor Thanos Athanasiou MD PhD MBA FECTS FRCS

Faculty of Medicine, Department of Surgery & Cancer

Professor of Cardiovascular Sciences
 
 
 
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Contact

 

t.athanasiou

 
 
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Location

 

1022Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Saitta:2023:10.1016/j.cmpb.2023.107468,
author = {Saitta, S and Maga, L and Armour, C and Votta, E and O'Regan, DP and Salmasi, MY and Athanasiou, T and Weinsaft, JW and Xu, XY and Pirola, S and Redaelli, A},
doi = {10.1016/j.cmpb.2023.107468},
journal = {Computer Methods and Programs in Biomedicine},
pages = {1--8},
title = {Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta.},
url = {http://dx.doi.org/10.1016/j.cmpb.2023.107468},
volume = {233},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 stu
AU  - Saitta,S
AU  - Maga,L
AU  - Armour,C
AU  - Votta,E
AU  - O'Regan,DP
AU  - Salmasi,MY
AU  - Athanasiou,T
AU  - Weinsaft,JW
AU  - Xu,XY
AU  - Pirola,S
AU  - Redaelli,A
DO  - 10.1016/j.cmpb.2023.107468
EP  - 8
PY  - 2023///
SN  - 0169-2607
SP  - 1
TI  - Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta.
T2  - Computer Methods and Programs in Biomedicine
UR  - http://dx.doi.org/10.1016/j.cmpb.2023.107468
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36921465
UR  - https://www.sciencedirect.com/science/article/pii/S0169260723001347?via%3Dihub
UR  - http://hdl.handle.net/10044/1/103317
VL  - 233
ER  -
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