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Data Assimilation Performed with Robust Shape Registration and Graph Neural Networks: Application to Aortic Coarctation by Romor, Francesco; Galarce, Felipe; Brüning, Jan; Goubergrits, Leonid; Caiazzo, Alfonso is a scholarly article available to read on EtoBox.

What is Data Assimilation Performed with Robust Shape Registration and Graph Neural Networks: Application to Aortic Coarctation about?

Image-based, patient-specific modelling of hemodynamics can improve diagnostic capabilities and provide complementary insights to better understand the hemodynamic treatment outcomes. However, computational fluid dynamics simulations remain relatively costly in a clinical context. Moreover, projection-based reduced-order models and purely data-driven surrogate models struggle due to the high variability of anatomical shapes in a population. A possible solution is shape registration: a reference template geometry is designed from a cohort of available geometries, which can then be diffeomorphically mapped onto it. This provides a natural encoding that can be exploited by machine learning architectures and, at the same time, a reference computational domain in which efficient dimension-reduction strategies can be performed. We compare state-of-the-art graph neural network models with recent data assimilation strategies for the prediction of physical quantities and clinically relevant biomarkers in the context of aortic coarctation.

Author

Romor, Francesco; Galarce, Felipe; Brüning, Jan; Goubergrits, Leonid; Caiazzo, Alfonso

Published

2025

Language

EN