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，其目的是在限制对周围正常组织的毒性的同时，提供高度局部化的体积杀瘤剂量。通常，调强放射治疗(IMRT)计划过程中的治疗参数被优化，并在预先计划的过程中提供，该过程可以从几天延长到数周。然而，由于软组织变形、肿瘤退化和器官运动而导致的解剖结构的日常变化，需要经常监测患者并进行适当的调整，以确保有效性和减少暴露风险。尽管车载成像技术取得了技术进步，但健壮和计算效率高的算法在实现这些技术的最佳利用方面仍然严重滞后，例如将治疗前计划图像实时配准到日常扫描、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，我们提出了一种基于统计学习技术的自适应反馈框架。这里的关键思想是保留先前调强放疗计划解的最优性条件，同时“绝热地”(以最小的损失或收益扰动)修改当前解。具体地说，我们计划使用快速统计增量学习方法来设计和实现流处理器在线可变形图像配准和IMRT优化，旨在使IMRT计划实时适应“当天的解剖”，并将这种方法扩展到运动补偿交付。我们将根据放射生物学和剂量学原理，推导和评估适应决策的新标准。我们将使用幻影和来自不同癌症部位的研究案例，严格评估建议的治疗计划适应反馈系统的性能。从技术上讲，这一建议将带来动态在线优化、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优的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.