Collaborative Research: CDS&E: An experimentally validated, interactive, data-enabled scientific computing platform for cardiac tissue ablation characterization and monitoring

University Of Kansas Center For Research Inc, Lawrence KS

Investigators

Abstract

Radiofrequency cardiac ablation therapy – the use of heat delivered to destroy abnormal heart tissue that causes rhythm disorders (performed using catheters inserted into veins or arteries) - provides an effective and, compared to heart surgery, a less invasive treatment option. Unfortunately, as many as 50% of ablation patients experience the return of disease due to incomplete tissue ablation. Successful treatment requires continuous ablation lines that induce permanent thermal damage to the tissue, achieved by sufficient exposure of the tissue to high enough temperatures to ensure cell death. The investigative team’s expertise in heat transfer theory, image computing and visualization, biomedical modeling and simulation, and experimental validation will be leveraged to better understand the physiological mechanisms that govern the transfer of heat into biological tissues by modeling and quantifying tissue responses to thermal energy. This research will help characterize the thermal injury delivered to the heart tissue during therapy, and will, therefore, have the potential to evolve into a future tool to better guide and monitor cardiac ablation therapy. This project also features a synergistically integrated education and outreach program that will foster research opportunities for graduate and undergraduate students in computer science, biomedical engineering, mathematics, and imaging science at Rochester Institute of Technology and the University of Kansas. The team will also develop innovative hands-on workshops to inspire and educate K-12 students from underrepresented groups on biomedical computing and medicine. Successful treatment of cardiac arrhythmia via ablation therapy requires the delivery of continuous ablation patterns that induce permanent thermal damage to the tissue, achieved by sufficient exposure to cell death temperatures and above. However, temperature readings inside the beating heart are invasive and infeasible. Hence, intrinsic knowledge of the heat transfer mechanisms and their effects on the tissue (e.g., temperature distribution, lesion geometry, quantification of induced thermal damage) is critical to understanding tissue response to thermal energy. Moreover, to enable intra-operative thermal monitoring, rapid, interactive characterization and visualization of the thermal lesions is equally critical. To better understand the physiological mechanisms that govern heat transfer into biological tissues, this project will capitalize on the investigating team’s cross-disciplinary expertise spanning fundamental heat transfer theory, image computing and visualization, biomedical modeling and simulation, scientific computing, and experimental validation to develop an intelligent computational framework to model and quantify tissue response to thermal energy. To rapidly characterize and visualize the ablation lesions, the team will research computationally-efficient thermal damage reversibility metrics operating in concert with voxel-derived, high-order meshing methods, which allow for rapid quantification of tissue temperature and lesion characterization. Previously developed numerical verification techniques will be utilized to assess the performance of the developed ablation modeling framework. Lastly, this project will also leverage the team’s expertise in building experimental test beds featuring in vitro constructs and ex vivo tissue samples to compare model-predictions and experimental lesions using infrared imaging, temperature measurements, and tissue staining. The developed framework will be released to the scientific computing community for research and educational use. 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.