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

放射性心脏消融治疗-使用热量破坏导致心律失常的异常心脏组织（使用插入静脉或动脉的导管进行）-提供了一种有效的治疗方案，与心脏手术相比，这种治疗方案侵入性更小。 不幸的是，多达50%的消融患者由于组织消融不完全而经历疾病复发。成功的治疗需要对组织造成永久性热损伤的连续消融线，通过将组织充分暴露于足够高的温度以确保细胞死亡来实现。研究团队在传热理论，图像计算和可视化，生物医学建模和仿真以及实验验证方面的专业知识将被利用，以更好地了解通过建模和量化组织对热能的反应来管理热量传递到生物组织的生理机制。这项研究将有助于表征治疗期间传递到心脏组织的热损伤，因此，有可能发展成为未来更好地指导和监测心脏消融治疗的工具。该项目还具有协同整合的教育和推广计划，将促进研究生和本科生在计算机科学，生物医学工程，数学和成像科学在罗切斯特理工学院和堪萨斯大学的研究机会。该团队还将开发创新的实践研讨会，以激励和教育来自生物医学计算和医学代表性不足的群体的K-12学生。通过消融治疗成功治疗心律失常需要输送连续消融模式，该模式通过充分暴露于细胞死亡温度及以上来诱导对组织的永久性热损伤。然而，跳动的心脏内的温度读数是侵入性的和不可行的。因此，热传递机制及其对组织的影响的固有知识（例如，温度分布、损伤几何形状、诱导热损伤的量化）对于理解组织对热能的响应至关重要。此外，为了实现术中热监测，热损伤的快速、交互式表征和可视化同样重要。为了更好地了解控制热传递到生物组织的生理机制，该项目将利用研究团队的跨学科专业知识，包括基础热传递理论，图像计算和可视化，生物医学建模和模拟，科学计算和实验验证，以开发一个智能计算框架来建模和量化组织对热能的反应。为了快速表征和可视化消融病变，该团队将研究计算效率高的热损伤可逆性指标，与体素衍生的高阶网格化方法协同工作，从而实现组织温度和病变表征的快速量化。以前开发的数值验证技术将被用来评估性能的消融建模框架。最后，该项目还将利用团队在构建实验测试床方面的专业知识，以体外构建和离体组织样本为特征，使用红外成像，温度测量和组织染色来比较模型预测和实验损伤。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.