In silico biomechanical design of the metal frame of transcatheter aortic valves: multi-objective shape and cross-sectional size optimization

Transcatheter aortic valve (TAV) implantation has become an established alternative to open-hearth surgical valve replacement. Current research aims to improve the treatment safety and extend the range of eligible patients. In this regard, computational modeling is a valuable tool to address these challenges, supporting the design phase by evaluating and optimizing the mechanical performance of the implanted device. In this study, a computational framework is presented for the shape and cross-sectional size optimization of TAV frames. Finite element analyses of TAV implantation were performed in idealized aortic root models with and without calcifications, implementing a mesh-morphing procedure to parametrize the TAV frame. The pullout force magnitude, peak maximum principal stress within the aortic wall, and contact pressure in the left ventricular outflow tract were defined as objectives of the optimization problem to evaluate the device mechanical performance. Design of experiment coupled with surrogate modeling was used to define an approximate relationship between the objectives and the TAV frame parameters. Surrogate models were interrogated within a fixed design space and multi-objective design optimization was conducted. The investigation of the parameter combinations within the design space allowed the successful identification of optimized TAV frame geometries, suited to either a single or groups of aortic root anatomies. The optimization framework was efficient, resulting in TAV frame designs with improved mechanical performance, ultimately leading to enhanced procedural outcomes and reduced costs associated with the device iterative development cycle.

Automated summary

This study presents a computational framework for optimizing the shape and cross-sectional size of TAV frames, aiming to improve the mechanical performance of the device. By utilizing finite element analyses and surrogate modeling, optimized TAV frame designs were successfully identified, leading to enhanced procedural outcomes and reduced development costs.