CAREER: GPU-Accelerated Framework for Integrated Modeling and Biomechanics Simulations of Cardiac Systems

职业：用于心脏系统集成建模和生物力学模拟的 GPU 加速框架

• 批准号: 1750865
• 负责人: Adarsh Krishnamurthy
• 金额: $ 50万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750865&HistoricalAwards=false
• 关键词: CAREER; GPU; Accelerated; Framework; Integrated

Cardiovascular diseases, such as heart failure, are one of the leading cause of death in the U.S. and pose a severe burden to the healthcare system. Most current treatments for cardiovascular diseases are based on rough estimates of outcomes from the results of clinical trials, which might not apply to individual patients due to patient-specific variations. Computational models of the cardiovascular system, developed from patient-specific clinical data, can help refine the diagnosis and personalize the treatment, significantly improving patient care and reducing mortality. The current patient-specific methods for cardiovascular diseases have been demonstrated mainly in simple, isolated examples. For widespread adoption of personalized medicine, a flexible and easy-to-use framework for integrating patient data and simulating cardiac biomechanics needs to be developed. This project focuses on creating an integrative framework with simulation, analysis, and visualization tools that will significantly advance the state-of-the-art in personalized medicine, ultimately improving patient care and treatment outcomes. Results from this research will benefit the U.S. healthcare system, society, and economy, while supporting the NSF mission to promote the progress of science and advance the national health. The tools developed as a part of this research involves several disciplines including computer science, bioengineering, and mechanical engineering. The multidisciplinary components of the project is being integrated into a larger educational effort that offers the students a solid foundation in developing computational tools and algorithms, while also broadening the participation of underrepresented groups in research.The primary objective of this research is the advancement of the state-of-the-art in translational medicine with the help of computational modeling and interactive analysis tools to improve the basic understanding of the cardiac muscle and personalize treatment of cardiovascular diseases in patients. The research focuses on creating a novel computational framework to automate biomechanics finite-element simulation and analysis of patient-specific cardiac systems. Further, it aims to advance the knowledge of disease and therapeutic mechanisms by developing advanced multiscale methods to model muscle contraction and growth. Some of the key computational tools and methods proposed as part of this framework include: (1) a geometric mesh generation tool for systematic generation of patient-specific finite element meshes from clinical data; (2) an algorithm for accelerating high-order finite-element simulations using the graphics processing unit (GPU) for fast tuning of model parameters to match the patients' baseline cardiac function; (3) new methods for multiphysics simulations of cardiac systems to model multi-scale muscle mechanics and tissue growth; and (4) new visualization and virtual reality tools to enable animated volume rendering and visual analytics of the results of the cardiac simulations. Successful development of these open-source tools will enable faster adoption of patient-specific computational models by the research community to understand therapeutic mechanisms. This framework can significantly advance the state-of-the-art in personalized medicine, ultimately improving patient care and treatment outcomes. The multidisciplinary components of the project is being integrated into a larger educational effort to offer students a solid foundation in combining biomedical engineering with scientific computing. The education and outreach plans of this research can inform the community about the crucial role of computational models in improving patient-care.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.

心血管疾病，如心力衰竭，是美国的主要死亡原因之一，并对医疗保健系统造成严重负担。目前大多数心血管疾病的治疗方法都是基于对临床试验结果的粗略估计，由于患者特异性差异，这些结果可能不适用于个体患者。根据患者特定的临床数据开发的心血管系统计算模型可以帮助完善诊断和个性化治疗，显著改善患者护理并降低死亡率。目前针对心血管疾病的患者特异性方法主要在简单、孤立的例子中得到证明。为了广泛采用个性化医疗，需要开发一个灵活且易于使用的框架，用于整合患者数据和模拟心脏生物力学。该项目的重点是创建一个具有模拟，分析和可视化工具的综合框架，这将显着推进个性化医疗的最新技术，最终改善患者护理和治疗效果。这项研究的结果将有利于美国的医疗保健系统，社会和经济，同时支持NSF的使命，以促进科学的进步和促进国民健康。作为本研究的一部分开发的工具涉及多个学科，包括计算机科学、生物工程和机械工程。该项目的多学科组成部分正在被整合到一个更大的教育工作，为学生提供了一个坚实的基础，在开发计算工具和算法，同时也扩大了代表性不足的群体在研究中的参与。这项研究的主要目标是促进国家的，艺术在转化医学的帮助下，计算建模和交互式分析工具，以提高对心肌的基本理解和个性化治疗心血管疾病，患者该研究的重点是创建一个新的计算框架，以自动化生物力学有限元模拟和分析患者特定的心脏系统。此外，它旨在通过开发先进的多尺度方法来模拟肌肉收缩和生长，从而提高对疾病和治疗机制的认识。作为该框架的一部分提出的一些关键计算工具和方法包括：（1）用于从临床数据系统地生成患者特定有限元网格的几何网格生成工具;（2）用于使用图形处理单元（GPU）加速高阶有限元模拟的算法，用于快速调整模型参数以匹配患者的基线心脏功能;（3）用于心脏系统的多物理场模拟的新方法，以模拟多尺度肌肉力学和组织生长;以及（4）新的可视化和虚拟现实工具，以实现心脏模拟结果的动画体积渲染和可视化分析。这些开源工具的成功开发将使研究界能够更快地采用患者特定的计算模型，以了解治疗机制。该框架可以显著推进个性化医疗的最新发展，最终改善患者护理和治疗效果。该项目的多学科组成部分正在被整合到一个更大的教育工作，为学生提供一个坚实的基础，结合生物医学工程与科学计算。这项研究的教育和推广计划可以告知社区计算模型在改善病人护理方面的关键作用。该奖项反映了NSF的法定使命，并被认为值得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来支持。

项目成果

Scalable Adaptive PDE Solvers in Arbitrary Domains

• DOI: 10.1145/3458817.3476220
• 发表时间: 2021-08
• 期刊: SC21: International Conference for High Performance Computing, Networking, Storage and Analysis
• 作者: K. Saurabh;Masado Ishii;Milinda Fernando;Boshun Gao;Kendrick Tan;M. Hsu;A. Krishnamurthy;H. Sundar;B. Ganapathysubramanian

GPU-Accelerated Post-Processing and Animated Volume Rendering of Isogeometric Analysis Results

GPU 加速的等几何分析结果的后处理和动画体积渲染

• DOI: 10.14733/cadaps.2022.779-796
• 发表时间: 2021
• 期刊: Computer-Aided Design and Applications
• 作者: Shah, Harshil;Huang, Xin;Bingol, Onur;Rajanna, Manoj;Krishnamurthy, Adarsh
• 通讯作者: Krishnamurthy, Adarsh

Direct 3D printing of multi-level voxel models

• DOI: 10.1016/j.addma.2021.101929
• 发表时间: 2021-04
• 期刊: Additive manufacturing
• 影响因子: 11
• 作者: Sambit Ghadai;Anushrut Jignasu;A. Krishnamurthy

Optimal surrogate boundary selection and scalability studies for the shifted boundary method on octree meshes

• DOI: 10.1016/j.cma.2023.116686
• 发表时间: 2023-12-20
• 期刊: COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
• 影响因子: 7.2
• 作者: Yang，Cheng-Hau;Saurabh，Kumar;Ganapathysubramanian，Baskar
• 通讯作者: Ganapathysubramanian，Baskar

THB-Diff: a GPU-accelerated differentiable programming framework for THB-splines

• DOI: 10.1007/s00366-023-01929-1
• 发表时间: 2023-12
• 期刊: Engineering with Computers
• 影响因子: 8.7
• 作者: A. Moola;Aditya Balu;A. Krishnamurthy;Aishwarya Pawar