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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.
- Not Site-Specific Cancer
Common Scientific Outline (CSO) Research Areas
- 5.1 Treatment Localized Therapies - Discovery and Development