Visual Modelling and Rendering for Virtual Reality Based Surgical Simulation

基于虚拟现实的手术模拟的视觉建模和渲染

• 批准号: RGPIN-2021-04193
• 负责人: Liu, Peter
• 金额: $ 2.84万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=765864
• 关键词: Visual; Modelling; Rendering; Virtual; Reality

The lack of effective methods and tools for surgical training has been a long-standing problem in medical education. Current approaches exhibit various limitations and drawbacks: animals have different anatomies from humans; cadavers are unable to provide physiological responses; it is risky for surgical residents to practice on patients. A recent CBC report shows that a great percentage of Canadian surgical residents do not demonstrate basic hands-on skills and manual dexterity.     Because of its many unique advantages, virtual-reality based surgical simulators are increasingly regarded as a promising solution to the current predicament, representing next-generation paradigm for surgical training. A surgical simulator is able to generate virtual patients with human anatomy and physiological responses, providing a risk-free training environment. In addition to these obvious benefits, surgery residents can practice on a wide range of pathological states with unlimited times; new complex surgical procedures can be tried safely on a surgical simulator before proceeding to patients.     Current surgical simulators, however, are still in their infancy and there exist numerous technical challenges and difficulties. In particular, no simulator is able to provide a sufficiently high degree of visual resemblance to real-life surgery. Aiming at solving this problem, this proposal will deal with some key issues about the visual feedback of surgical simulation: physical modelling (i.e., the modelling of soft tissue's biomechanical properties), geometric modelling (the modelling of soft tissue's deformation) and visual rendering. The anticipated results will be implemented and validated in the development of a neurosurgery simulator prototype.     The long-term goal is to develop principles, algorithms and tools that are essential to realize highly realistic and naturalistic visual feedback for surgical simulation, enabling educational institutions and hospitals to train surgeons (particularly neurosurgeons) using virtual-reality based simulators. To achieve this goal, specific research objectives are as follows:     - To develop suitable models to incorporate anisotropic, viscoelastic and physiological features, improving the modelling accuracy of the physical (biomechanical) properties of human soft tissue;     - To explore to extract the biomechanical parameters of human soft tissue using CT and/or MRI images, addressing the dilemma due to various difficulties associated with current methods;     - To develop a meshless approach to geometric models for the deformation of human soft tissue;     - To develop rendering methods to mix/integrate seamlessly simulated surgical scenes with real surgical video as the background, achieving high degree of visual immersion;      - To apply anticipated results to develop a new neurosurgery simulator in collaboration with the industrial partner and clinical practitioner.

缺乏有效的外科培训方法和工具一直是医学教育中的一个长期问题。目前的方法存在各种局限性和缺点：动物的解剖结构与人类不同；尸体无法提供生理反应，这对外科住院医生来说是有风险的。加拿大广播公司 (CBC) 最近的一份报告显示，很大一部分加拿大外科住院医师没有表现出基本的动手技能和手工灵巧性，基​​于虚拟现实的手术模拟器越来越被认为是一种技术。这是解决当前困境的一个有希望的解决方案，代表了下一代手术培训范例，能够生成具有人体解剖学和生理反应的虚拟患者，除了这些明显的好处外，还为手术住院医师提供了无风险的培训环境。可以无限次地在各种病理状态下进行练习；在对患者进行手术之前，可以在手术模拟器上安全地尝试新的复杂手术程序。​然而，目前的手术模拟器仍处于起步阶段，存在许多技术挑战和困难。 。特别是，没有模拟器能够提供与现实生活中的手术足够高的视觉相似度。为了解决这个问题，本提案将处理有关手术模拟的视觉反馈的一些关键问题：物理建模（即，软组织生物力学特性建模）、几何建模（软组织变形建模）和视觉渲染将在神经外科模拟器的开发中得到实现和验证。​长期目标是开发对于实现手术模拟高度真实和自然的视觉反馈至关重要的原理、算法和工具，使教育机构和医院能够使用基于虚拟现实的模拟器来培训外科医生（特别是神经外科医生）。为了实现这一目标，具体研究目标如下： - 开发合适的模型来融合各向异性、粘弹性和生理特征，提高人体软体物理（生物力学）特性的建模精度- 探索利用 CT 和/或 MRI 图像提取人体软组织的生物力学参数，解决现有方法带来的各种困难； - 开发人体软组织变形的无网格方法；开发渲染方法，将模拟手术场景与真实手术视频作为背景无缝混合/集成，实现高度的视觉沉浸感 - 应用预期结果开发新的模型；与工业合作伙伴和临床医生合作的神经外科模拟器。