Collaborative Research: Understanding Ablation Through Accurate Integration of Models and in Vivo Experimental Data

About This Grant

Many types of disease can be treated with ablation, a medical procedure which applies energy to destroy small regions of tissue that do not behave normally. Ablation therapy can be used to treat conditions like arthritis, uterine fibroids, and cancer. It can also treat disruptions of the heart’s regular rhythm, such as atrial fibrillation. Ablation procedures can be difficult to perform, and sometimes multiple treatments may be necessary. A deeper understanding of exactly how the settings associated with the ablation procedure affect the biological tissue could lead to better results. This project aims to improve the understanding of radiofrequency ablation’s interactions with heart tissue through a combination of theory, multi-physics and machine-learning models, and experiments. To ensure the experiments reflect the differences in tissue structures and properties of real patients, tissue from human hearts no longer needed after being replaced by transplants will be used when possible. Medical doctors will help assess the practical significance of the project’s results. This study has the potential to lead to improved ablation treatments and patient outcomes, and the new methodology can be extended, with minor adaptations, to other types of diseases. Educational components include training of graduate and undergraduate students, contributions to undergraduate and graduate courses, and engagement of the general public with interactive programs available through a website. Radiofrequency ablation (RFA), used for a wide variety of physiological systems, faces limitations from an imprecise understanding of ablation and tissue interactions, along with challenges in optimizing the procedure given the many parameters associated with ablation and patient variability. This project aims to develop and validate a detailed multi-physics mathematical RFA model with an unprecedented level of accuracy and analysis. It will focus on cardiac tissue, but the tools can be adapted for other biological tissues and ablation therapies. First, the novel computational model will include advanced methods of domain decomposition and model reduction to address the multi-physics nature of the problem and will incorporate important physiological parameters of ablation-tissue interactions. Second, the model will be enhanced by rigorously integrating the sizes, thicknesses and thermal profiles of ablation lesions in cardiac tissue from varying thermal doses, contact angles, and pressures and by comparing with experiments. This project will be enhanced by using optical-mapping methods during ablation in live hearts, including live human explanted hearts from patients undergoing heart transplants, to simultaneously quantify the extent and sensitivity of the ablation at different tissue depths in real time as a function of ablation parameters. This information will enable continuous refinement of the computational model and accurate sensitivity analysis. Finally, simulations and experiments will be integrated to assess how ablation lesions will effectively terminate disorganized electrical wave propagation during fibrillation. The mechanistic RFA model will provide highly accurate predictions of ablation parameter effects on the success rate of terminating cardiac fibrillation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.