New tools and method for monitoring ionizing radiation delivery in medical physics

监测医学物理中电离辐射传递的新工具和方法

• 批准号: RGPIN-2018-04055
• 负责人: Archambault, Louis
• 金额: $ 2.48万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712731
• 关键词: New; tools; method; monitoring; ionizing

The goal of this research is to develop tools to improve the efficiency of radiation treatments. We will do this by following two distinct strategies. The first strategy is to develop new dose measuring devices capable of rapidly, accurately and precisely measuring the complex radiation dose delivered by state-of-the-art radiotherapy techniques such as radiosurgery. The first device is a detector capable of measuring radiation doses in three dimensions and as a function of time. The success of radiotherapy requires an accurate delivery of radiation dose that tightly match the shape of a tumor. Thus, 3D dose measurement is the best possible way of guaranteeing that radiotherapy can indeed reaches its target as planned. A second 3D detector will be built to study the impact anatomical changes during radiotherapy. Anatomical changes can reduces the efficacy of radiation treatments. It is therefore important to quantitatively evaluate their impact. While these impact can be estimated by computer simulations, there is currently no instrument that can directly measure them as a function of time. These new detector devices and the analysis software we will develop to interpret their results will have the potential to help medical physicists and other professionals to better control the accuracy of cancer treatments. The goal of our second strategy is to build a virtual safety net with smart algorithms that automatically analyze images produced during radiation treatments to guarantee that treatments are delivered accurately. Most radiotherapy delivery machines are equipped with X-ray imaging capability. In principle, these images can be used to monitor patients, but doing so is time and resource intensive. Furthermore, human-based monitoring is often inconsistent. Therefore, such imaging capability are often underused, especially in the context of busy Canadian radiotherapy clinics. By conceiving a prediction model, we expect to detect potential problems before they have a chance of altering treatment quality. This will form the basis of agile radiotherapy, a new treatment paradigm that aims to anticipate changes in radiotherapy patients through an image processing technique known as radiomics and then automatically suggest possible course of actions to efficiently correct problems as early as possible. These smart algorithms could eventually be included in the radiotherapy workflow and act as a decision support system that will help guide medical physicists and even physicians.

这项研究的目的是开发提高辐射治疗效率的工具。我们将遵循两种不同的策略来做到这一点。第一种策略是开发能够快速，准确，准确地测量最先进的放射治疗技术（例如放射外科手术）提供的复杂辐射剂量的新剂量测量设备。第一个设备是能够在三个维度和时间的函数中测量辐射剂量的检测器。放射疗法的成功需要准确的辐射剂量，与肿瘤的形状紧密匹配。因此，3D剂量测量是确保放疗确实可以按计划达到其靶标的最佳方法。将建立第二个3D检测器，以研究放射疗法期间的解剖学变化。解剖变化可以降低辐射处理的功效。因此，定量评估其影响很重要。尽管可以通过计算机模拟来估算这些影响，但目前尚无工具可以直接测量它们作为时间的函数。这些新的探测器设备和分析软件我们将开发以解释其结果，将有可能帮助医疗物理学家和其他专业人员更好地控制癌症治疗的准确性。 我们的第二个策略的目的是使用智能算法构建虚拟安全网，该算法自动分析辐射治疗过程中产生的图像，以确保治疗能够准确地提供。大多数放射疗法输送机都配备了X射线成像能力。原则上，这些图像可用于监测患者，但这样做是时间和资源密集型。此外，基于人类的监测通常不一致。因此，这种成像能力通常是不足的，尤其是在繁忙的加拿大放射治疗诊所的情况下。通过构想预测模型，我们希望在可能改变治疗质量的机会之前检测到潜在的问题。这将构成敏捷放射疗法的基础，这是一种新的治疗范式，旨在通过称为放射线学的图像处理技术预测放疗患者的变化，然后自动提出可能尽早有效地纠正问题的方法。这些智能算法最终可以包含在放射治疗工作流程中，并充当决策支持系统，将有助于指导医疗物理学家甚至医生。