Robust automated calcification meshing for biomechanical cardiac digital twins
<|reference_start|>Robust automated calcification meshing for biomechanical cardiac digital twins: Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcified heart meshes for physics-driven simulations are still often reconstructed using manual operations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated meshing algorithm that enables robust incorporation of patient-specific calcification onto a given heart mesh. The algorithm provides a substantial speed-up from several hours of manual meshing to $\sim$1 minute of automated computation, and it solves an important problem that cannot be addressed with recent template registration-based heart meshing techniques. We validated our final calcified heart meshes with extensive simulations, demonstrating our ability to accurately model patient-specific aortic stenosis and Transcatheter Aortic Valve Replacement. Our method may serve as an important tool for accelerating the development and usage of physics-driven simulations for cardiac digital twins.<|reference_end|>
@article{pak2024robust,
    title={Robust automated calcification meshing for biomechanical cardiac digital
  twins},
    author={Daniel H. Pak, Minliang Liu, Theodore Kim, Caglar Ozturk, Raymond
  McKay, Ellen T. Roche, Rudolph Gleason, James S. Duncan},
    journal={arXiv preprint arXiv:2403.04998},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04998},
    primaryClass={cs.CE cs.CV}
}