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

这项研究的目的是开发工具，以提高放射治疗的效率。为此，我们将采取两种不同的策略。第一个战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（如放射外科）提供的复杂辐射剂量。第一种装置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输送与肿瘤形状紧密匹配的辐射剂量。因此，3D剂量测量是保证放射治疗确实能够按计划到达目标的最佳方式。将建立第二个3D探测器，以研究放射治疗期间的影响解剖变化。解剖结构的变化会降低放射治疗的效果。因此，必须对其影响进行定量评估。虽然这些影响可以通过计算机模拟来估计，但目前还没有仪器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我们将开发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。** 我们第二个战略的目标是建立一个虚拟的安全网，使用智能算法自动分析放射治疗期间产生的图像，以确保治疗的准确性。大多数放射治疗输送机器配备有X射线成像能力。原则上，这些图像可以用于监测患者，但这样做是时间和资源密集型的。此外，以人为本的监测往往不一致。因此，这种成像能力通常未得到充分利用，特别是在忙碌的加拿大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。这将成为敏捷放射治疗的基础，这是一种新的治疗模式，旨在通过称为放射组学的图像处理技术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以被纳入放射治疗工作流程，并作为决策支持系统，帮助指导医学物理学家甚至医生。

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.