Generation of Shape Models of Calcified TAVR populations for Solid Mechanics Simulations by means of Deep Learning

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Abstract Minimally invasive transcatheter aortic valve replacement (TAVR) procedure has become the preferred procedure for patients with aortic valve stenosis or insufficiency with high risk for conventional open surgery. The favorable clinical outcomes of high-risk patients led to an expansion of the cohort including intermediate and low-risk patients. A critical aspect of advancing TAVR procedures lies in preoperative planning, integrating patient-specific in-silico deployment simulation and post-deployment fluid mechanics assessments. This study introduces a novel approach to calcified TAVR patient shape modeling, addressing this problems. The model integrates an extended mesh generated by DeepCarve, encompassing the aortic arch, and a novel deep learning-based volumetric shape model of calcifications. The key innovation lies in the utilization of a conditional Convolutional Variational Autoencoder (cCVAE) to generate realistic calcification patterns, demonstrating promising preliminary results in matching actual cohort data. Future investigations should focus on data collection from diverse medical centers to validate and refine the proposed methodology. This study showcases significant progress in generating synthetic TAVR patient geometries, incorporating detailed anatomical structures such as the aortic root, valve, and arch, along with volumetric calcification patterns. These findings represent a crucial step towards enabling real-time preoperative TAVR planning, inclusive of patient- specific in-silico deployment simulation and comprehensive fluid mechanics assessments.

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