Computational methods for planning of image-guided surgery methods

图像引导手术方法规划的计算方法

• 批准号: RGPIN-2019-05063
• 负责人: Kunz, Manuela
• 金额: $ 2.04万
• 依托单位: Queen's 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=750624
• 关键词: Computational; methods; planning; image; guided

The proposed research aims to improve surgical interventions using innovative software methods for image-guided surgery. During image-guided interventions, medical images of the affected anatomy are obtained prior to surgery and a 3D virtual model of the anatomy is created by extracting relevant data from these images. This model is then used in specially designed software to support the surgeon in pre-planning the procedure. During this planning, the surgeon makes decisions such as the required size of the incision, which prosthesis components are optimal for the patient, and which instrument positions and trajectories are needed to ensure the best outcome. During surgery, tracking methods are provided in the operating theatre as a navigation aid for the surgeon in following the planned instrument trajectories. This research program will focus on novel and advanced methods to improve pre-surgery planning. One theme involves development and testing of computational methods to generate more accurate representations of the affected anatomy in the 3D computer generated model. A second theme of this research program will investigate computational methods to create more accurate and clinically relevant planning of prosthesis component size, position, and/or instrument trajectories. This will involve building a mathematical model for the process of surgical decision-making and using this model to design planning systems to support the surgeon more effectively. Artificial intelligence will be employed to identify patterns between planning parameters and clinical outcomes for surgeries for a large population of patients. These patterns can then predict clinical outcomes based on virtual planning of the surgery. By providing a safe and easy-to-use online system, experiences from a wide community of surgeons can be used in developing each new plan. The proposed program will train graduate and undergraduate students in a highly interdisciplinary environment in software development, medical image modalities, image analysis algorithms, statistical methods, artificial intelligence methods, and graphical user interface design. The proposed research has the potential to improve the outcome for surgical interventions, reduce the number of revision or repeat surgeries, make surgical systems more user-friendly, and improve the efficiency in the surgery room. At a time when health care costs are steadily rising and the number of orthopaedic procedures is expected to double in coming years, the proposed research can provide economic solutions for surgical interventions. Just as today we expect that every car be equipped with a navigation device, this research program will be part of a future in which every surgical intervention will be supported by an image-guided navigation device.

拟议的研究旨在使用创新的图像引导手术软件方法来改善手术干预。在图像引导介入期间，在手术之前获得受影响解剖结构的医学图像，并且通过从这些图像提取相关数据来创建解剖结构的3D虚拟模型。然后，该模型用于专门设计的软件中，以支持外科医生预先计划手术。在此计划过程中，外科医生做出决定，例如所需的切口尺寸，哪些假体组件对患者最佳，以及需要哪些器械位置和轨迹来确保最佳结果。在手术期间，在手术室中提供跟踪方法作为外科医生遵循计划的器械轨迹的导航辅助。 该研究计划将专注于新的和先进的方法，以改善术前规划。一个主题涉及开发和测试计算方法，以在3D计算机生成模型中生成受影响解剖结构的更准确表示。 该研究计划的第二个主题将研究计算方法，以创建更准确和临床相关的假体组件尺寸，位置和/或器械轨迹的规划。这将涉及建立一个数学模型的过程中的手术决策和使用这个模型来设计规划系统，以支持外科医生更有效地。人工智能将用于识别大量患者手术的计划参数和临床结果之间的模式。然后，这些模式可以基于手术的虚拟规划来预测临床结果。通过提供一个安全且易于使用的在线系统，来自广泛外科医生社区的经验可以用于制定每个新计划。该计划将在软件开发，医学图像模式，图像分析算法，统计方法，人工智能方法和图形用户界面设计的高度跨学科环境中培养研究生和本科生。 拟议的研究有可能改善手术干预的结果，减少翻修或重复手术的数量，使手术系统更加用户友好，并提高手术室的效率。在医疗保健成本稳步上升，骨科手术数量预计将在未来几年翻一番的时候，拟议的研究可以为外科干预提供经济的解决方案。就像今天我们期望每辆汽车都配备导航设备一样，这项研究计划将成为未来的一部分，在未来，每一次手术干预都将得到图像引导导航设备的支持。