A real-time framework for image-guided adaptive radiotherapy

电离辐射是癌症治疗的一种重要方式，其目的是提供高度局部化的体积杀肿瘤剂量，同时限制对周围正常组织的毒性。 通常，在强度调制放射治疗（IMRT）计划过程中的治疗参数被优化并且在可以从几天延伸到多周的预先计划的过程中被递送。然而，由于软组织变形、肿瘤消退和器官运动导致的解剖结构的日常变化需要对患者进行频繁监测和适当调整，以确保有效性并降低暴露风险。 尽管机载成像技术取得了技术进步，但稳健且计算高效的算法仍然严重滞后，无法实现这些技术的最佳利用，例如实时将治疗前计划图像配准到日常扫描、使剂量变形以及仍然重新优化计划以满足原始治疗多目标目标的能力。已经提出了一些简单的权衡方法，这些方法可能会导致有害的陷阱。为了克服这些挑战，我们提出了一个新的自适应反馈框架的基础上统计学习技术。 这里的关键思想是保留先前IMRT计划解决方案的最优性条件，同时“以最小的损失或增益扰动”修改当前解决方案。具体来说，我们计划使用快速统计增量学习方法在流处理器上设计和实现在线可变形图像配准和调强放射治疗优化，旨在实时调整调强放射治疗计划以适应“当天的解剖结构”，并将这种方法扩展到运动补偿输送。我们将根据放射生物学和剂量学原理推导和评估适应性决策的新标准。我们将严格评估所提出的反馈系统的性能，治疗计划的适应使用phantomy和研究病例从不同的癌症部位。从技术上讲，该提案将产生用于动态在线优化、可变形图像跟踪、运动预测和决策的新方法，同时在临床上允许最佳个性化治疗计划适应和改善结果。

Ionizing radiation is an important modality for cancer treatment, which aims to deliver a highly localized volumetric tumoricidal doses while limiting toxicity to surrounding normal tissues. Typically, treatment parameters in an Intensity Modulated Radiotherapy (IMRT) planning process are optimized and delivered over a pre-planned course that can extend from few days to multiple weeks. However, daily changes in anatomy due to soft-tissue deformation, tumor regression, and organ motion require frequent monitoring of patients and proper adjustment to ensure effectiveness and reduce exposure risks. Despite technological advances in on-board imaging technologies, robust and computationally efficient algorithms are still severely lagging to achieve the optimal utilization of these technologies such as the ability in real-time to register the pre-treatment planning images to the daily scans, deform the dose, and still re-optimize the plan to satisfy the original treatment multi-objective goals. Simple trade-off methods have been proposed that can lead to detrimental pitfalls. To overcome these challenges, we propose a new adaptive feedback framework based on statistical learning techniques. The key idea here is to retain the optimality conditions of the previous IMRT plan solution while "adiabatically" (perturbing with minimal loss or gain) modifying the current solution. Specifically, we plan to design and implement on stream processors online deformable image registration and IMRT optimization using fast statistical incremental learning approaches that aims to adapt IMRT plans to 'anatomy of the day' in real-time and extend this approach to motion-compensated delivery. We will derive and evaluate new criteria for adaptation decision-making based on radiobiological and dosimetric principles. We will rigorously evaluate the performance of the proposed feedback system for treatment planning adaptation using phantoms and study cases from different cancer sites. Technically, this proposal will result in new methods for dynamical online optimization, deformable image tracking, motion prediction, and decision making while clinically allowing optimal personalized treatment planning adaptation and improved outcomes.