SCH: INT: A Virtual Surgery Simulator to Accelerate Medical Training in Cardiovascular Disease

SCH：INT：加速心血管疾病医疗培训的虚拟手术模拟器

• 批准号: 10412769
• 负责人: Alison L Marsden
• 金额: $ 31.49万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-30 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10412769
• 关键词: 3-Dimensional; Address; Adoption; Anatomic Models; Anatomy; Artificial Intelligence; Biomechanics; Blood Vessels; Blood flow; Cardiac; Cardiovascular Diseases; Cardiovascular Models; Cardiovascular system; Classification; Clinical; Clinical Research; Collaborations; Communities; Computer Graphics; Computer software; Data; Data Science; Data Set; Databases; Development; Disease; Education; Elements; Ensure; FAIR principles; Feedback; Fostering; Funding; Goals; Gold; Grant; Hand; High Performance Computing; Hour; Image; Industry; International; Intuition; Investments; Joints; Knowledge acquisition; Lead; Machine Learning; Medical; Medical Device Designs; Medical Imaging; Medical Students; Medicine; Methods; Modeling; Neck; Operative Surgical Procedures; Parents; Pathology; Patients; Performance; Physics; Physiology; Process; Recording of previous events; Research; Research Personnel; Resources; Risk Assessment; Running; Science; Source Code; Technology; Time; Training; Twin Multiple Birth; United States National Institutes of Health; United States National Library of Medicine; Visualization; base; cluster computing; cohort; computing resources; data format; data repository; data resource; data structure; deep learning; design; digital; educational atmosphere; experience; imaging Segmentation; imaging modality; improved; insight; interactive feedback; interest; learning network; learning strategy; meetings; models and simulation; novel; open data; open source; prevent; repository; science education; simulation; software development; symposium; three-dimensional modeling; time use; tool; treatment planning; virtual reality; virtual surgery

The objective of the parent proposal is to devise and deploy an integrated virtual surgery simulator to transform training and surgical planning in cardiovascular medicine. By advancing science in graphics, visualization and real-time simulations, and interfacing with virtual reality (VR) technology, we will offer clinical trainees insight into cardiac physiology and pathology, accelerating knowledge acquisition and intuition-building in ways not possible with current tools. Despite recent advances in cardiovascular patient-specific modeling and blood flow simulation, current virtual surgery capabilities are limited to cumbersome by-hand model manipulations and blood flow simulations run on high-performance computing clusters for days at a time. These complexities preclude hands- on use by clinicians and often limit the models to a small cohort of anatomic designs. To drive this technology, the parent proposal contained the following specific aims: 1) Develop computer-graphics tools for efficient model manipulation, 2) Integrate reduced-order modeling with visualization to create a real-time interactive experience, and 3) Develop and deploy an interactive VR educational environment for medical students and clinical trainees. In support of these efforts, in this supplement proposal we will ready the Vascular Model Repository (VMR) for use in artificial intelligence (AI) and machine / deep learning (ML/DL) applications. The VMR is an open database of medical image data, segmented vascular models, and blood flow simulation results developed with support from the National Library of Medicine. We will use the VMR to support two major AI/ML efforts in the community. First, we will greatly accelerate anatomic model construction by DL for image segmentation, overcoming a major bottle neck to studies with large cohorts of patients. Second, we will develop physics-informed ML methods to drastically reduce current lengthy simulation times and provide fast and interactive feedback for surgical and interventional planning. This will produce fast and deployable ``digital twins'' that can provide interactive feedback to clinicians; these methods will be in direct support of the parent proposal and of general interest to the field. The supplement proposal contains three specific aims: 1) To ready the VMR for AI/ML by users who are not domain experts and spark interest in cardiovascular applications in the ML community, 2) To run community challenges in image segmentation and physics-informed ML at a major international meeting to identify best in class ML/DL methods, and 3) To demonstrate ML/DL methods in interactive surgical planning applications by integrating efforts with the parent proposal. We will disseminate our findings, methods, data, and source code to the research community via the open source SimVascular software project and the open data VMR. Our team brings together expertise in cardiovascular biomechanics and finite element modeling, computer graphics and reduced order modeling, physics informed ML, and development of open source and open data resources for the scientific community.

父提案的目标是设计和部署一个集成的虚拟手术模拟器， 心血管内科的培训和手术规划。通过推进图形、可视化和 实时模拟，并与虚拟现实（VR）技术接口，我们将提供临床实习生洞察到 心脏生理学和病理学，以不可能的方式加速知识获取和直觉建立 目前的工具。尽管最近在心血管患者特定建模和血流模拟方面取得了进展， 目前的虚拟手术能力仅限于繁琐的手动模型操作和血液流动 模拟在高性能计算集群上一次运行数天。这些复杂性排除了手- 并且通常将模型限制为一小群解剖设计。为了推动这项技术， 原提案包括以下具体目标：1）开发计算机图形工具，以实现高效的模型 2）将降阶建模与可视化集成，以创建实时交互式体验， 以及3）为医学生和临床实习生开发和部署交互式VR教育环境。 为了支持这些努力，在本补充提案中，我们将准备血管模型库（VMR）， 用于人工智能（AI）和机器/深度学习（ML/DL）应用。VMR是一个开放的数据库 医学图像数据、分割的血管模型和血流模拟结果， 从国家医学图书馆。我们将使用VMR来支持社区中的两个主要AI/ML工作。 首先，我们将大大加快解剖模型的建设DL图像分割，克服了一个主要的 大患者队列研究的瓶颈。第二，我们将开发基于物理的ML方法， 大幅减少当前冗长的模拟时间，并为手术和 介入计划这将产生快速和可部署的"数字孪生“，可以提供互动反馈 这些方法将直接支持母提案，并对该领域具有普遍意义。 该补充提案包含三个具体目标：1）为不具备AI/ML的用户准备VMR 领域专家，并在ML社区中激发对心血管应用的兴趣，2）运行社区 在一个重要的国际会议上，我们将讨论图像分割和物理信息ML方面的挑战， 类ML/DL方法，以及3）通过以下方式演示交互式手术计划应用程序中的ML/DL方法： 将努力与母提案结合起来。我们将把我们的发现、方法、数据和源代码传播给 通过开源SimVascular软件项目和开放数据VMR，我们的团队 汇集了心血管生物力学和有限元建模，计算机图形学和 降阶建模，物理学通知ML，以及开源和开放数据资源的开发， 科学界。