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.

这项研究的目标是开发提高放射治疗效率的工具。我们将通过遵循两种不同的策略来做到这一点。第一个策略是开发新的剂量测量设备，能够快速、准确和精确地测量由最先进的放射治疗技术（例如放射外科）提供的复杂辐射剂量。第一个设备是能够测量三维辐射剂量并作为时间函数的探测器。放射治疗的成功需要精确地输送与肿瘤形状紧密匹配的放射剂量。因此，3D剂量测量是保证放疗确实能够按计划达到目标的最佳方式。将建造第二个 3D 探测器来研究放射治疗期间的影响解剖学变化。解剖结构的变化会降低放射治疗的功效。因此，定量评估其影响非常重要。虽然这些影响可以通过计算机模拟来估计，但目前没有仪器可以直接测量它们随时间的变化。这些新的探测器设备和我们将开发用于解释其结果的分析软件将有可能帮助医学物理学家和其他专业人员更好地控制癌症治疗的准确性。 我们第二个策略的目标是建立一个具有智能算法的虚拟安全网，自动分析放射治疗过程中产生的图像，以确保准确地提供治疗。大多数放射治疗机都配备了 X 射线成像功能。原则上，这些图像可用于监测患者，但这样做需要大量时间和资源。此外，基于人的监控往往不一致。因此，这种成像能力常常没有得到充分利用，特别是在加拿大繁忙的放射治疗诊所的背景下。通过构建预测模型，我们希望在潜在问题有机会改变治疗质量之前发现它们。这将构成敏捷放射治疗的基础，这是一种新的治疗范式，旨在通过称为放射组学的图像处理技术来预测放射治疗患者的变化，然后自动建议可能的行动方案，以尽早有效地纠正问题。这些智能算法最终可能会被纳入放射治疗工作流程中，并充当决策支持系统，帮助指导医学物理学家甚至医生。