Impact of Calcified Plaque Material Properties on TAVI Performance: A Finite Element Analysis

Document Type : Original Article

Authors

1 Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, P. O. Box: 8415683111, Iran

2 -Department of Mechanical Engineering, Jundi-Shapur University of Technology, Dezful, Iran. - Department of Mechanical Engineering, Qom University of Technology, Qom, Iran

Abstract

Transcatheter aortic valve implantation (TAVI) has revolutionized the treatment of aortic stenosis, offering a minimally invasive alternative to traditional open-heart surgery. Despite its advantages, TAVI procedures are still associated with substantial complications, including embolism, paravalvular leak, aortic root rupture, and prosthesis migration. To enhance procedural safety and efficacy, advanced computational simulations are increasingly being employed as powerful tools to aid clinicians in pre-operative planning and mitigate potential risks. In this paper, a patient-specific approach was utilized to reconstruct a high-fidelity 3D model of a patient's heart from CT scan images using Mimics software. To achieve this, three distinct finite element simulations were performed to model the TAVI implantation process under various conditions; a healthy valve without calcification, and valves with calcified plaques exhibiting two different mechanical properties. The simulation results demonstrated that the presence and specific mechanical characteristics of calcified plaques within the native aortic valve profoundly impact the stress distribution and structural deformations of both the host cardiac tissue and the prosthetic valve. Specifically, calcified lesions significantly altered the biomechanical environment, leading to localized stress concentrations and altered leaflet coaptation. This research underscores the critical importance of accurately incorporating the mechanical properties of calcified plaques into computational models for precise prediction of implanted prosthetic valve behavior and optimization of TAVI outcomes. These findings contribute valuable insights for personalized procedural planning and the development of next-generation TAVI devices.

Keywords

Main Subjects

References

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