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bioRxiv · 10.1101/2025.03.05.641637

Aortic annuloplasty FSI digital twin of 3D-printed phantoms with 4D-flow MRI comparison

Bontempi, L.·Zattoni, M.·Ramella, A.·Migliavacca, F.·Ringgaard, S.·Kim, W. Y.·Johansen, P.·Colombo, M.
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Abstract

BackgroundAortic annuloplasty, involving the implantation of an external ring around the aortic root to reduce annular dimensions, is a promising treatment for aortic valve insufficiency. However, its hemodynamic effects remain underexplored due to the absence of computational models validated by experimental and clinical data. MethodsThis study introduces a computational fluid-structure interaction (FSI) model of supra valvular aortic annuloplasty using 4D-flow magnetic resonance imaging (MRI). Native and post-annuloplasty conditions of idealized aortic root phantoms, including the aortic valve, were CAD-modelled and 3D-printed with elastic resin. These phantoms were tested in a mock circulatory flow-loop providing normal pulsatile physiologic conditions using a glycerol-water mixture to simulate blood viscosity. Flow and pressure data collected from sensors were used as boundary conditions for FSI simulations. Experimental velocity fields from 4D-flow MRI were compared to computational results to assess model accuracy. ResultsMRI scans of the annuloplasty model showed an increased peak systolic velocity (up to 145.4 cm/s) and localized flow alterations, corresponding to a higher pressure gradient across the valve. During regurgitation, the annuloplasty model showed broader velocity distributions compared to the native condition. The FSI simulations closely matched 4D-flow MRI data, with strong correlation coefficients (r > 0.93) and minimal Bland-Altman differences, particularly during systolic phases. ConclusionsThis study establishes an integrative methodology combining in-vitro, in-silico, and clinical imaging techniques to evaluate aortic annuloplasty hemodynamics. The validated digital twin framework offers a pathway for patient-specific modelling, enabling prediction of surgical outcomes and optimization of aortic valve repair strategies.

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Bontempi, L., Zattoni, M., Ramella, A., Migliavacca, F., Ringgaard, S., Kim, W. Y., Johansen, P., Colombo, M.. 2025-03-10. Aortic annuloplasty FSI digital twin of 3D-printed phantoms with 4D-flow MRI comparison. https://doi.org/10.1101/2025.03.05.641637

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