ArticleComputer methods and programs in biomedicine2026
In silico modelling of aortic annuloplasty: hemodynamic assessment through in vitro experiments and in vivo MRI.
Article in Computer methods and programs in biomedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aortic annuloplasty (AA) is an innovative surgical technique for aortic root (AR) enlargement. It is performed by implanting sutures, bands, or rings, either externally or internally the AR, hereby reducing its diameter. This study evaluates the impact of AA approaches on AR hemodynamic by employing a porcine-specific workflow combining in vivo magnetic resonance imaging (MRI), in vitro experiments and in silico fluid-structure interaction (FSI) simulations investigating external single ring AA. CAD models of native and post-annuloplasty ARs were segmented from in vivo porcine MRI data and served as the basis for fabricating 3D-printed resin phantoms and implementing computational digital twins. The former were tested on a pulsatile flow-loop, whereas the latter were integrated in FSI simulations, with time-dependent boundary conditions based on the resultant experimental pressure waveforms. Additionally, a proof-of-concept validation of the in silico model against in vivo data is proposed. Computational results of the two cases were compared in terms of fluid velocity, vorticity, helicity, and wall shear stresses, providing a step towards understanding the complex interactions between the AR and blood flow dynamics. Results suggested that the presence of the ring increased the systolic jet flow and post-valve velocities (three-fold increase), reduced the backward, vortical flow during diastole (∼ 9% decrease), and induced modifications in bulk flow and wall shear stresses distribution. Furthermore, the development of an animal-specific digital twin of a post-AA AR represents a significant advancement in the field, providing a valuable tool for future research and for clinical applications to aid AA decision-making process.
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