Efficient and Intelligent Radiotherapy Planning and Adaptation

Treatment planning--inverse planning on volumetric tomography images--is the foundation of modern radiotherapy. Contouring planning structures and optimizing plan dose distributions are the two most important components of treatment planning. Treatment planning happens in two stages. The initial treatment plan is first designed before the commencement of treatment course. With the on-line images acquired before each fraction of treatment, treatment plan may be adapted off-line/on-line accounting for inter/intra-fractional changes. This dissertation presents innovative research to improve the efficiency and intelligence of treatment planning, with a special focus on its application in on-line adaptive planning. Four standalone, but related, techniques are developed in this dissertation.

• Registration-guided on-line image segmentation. This technique targets the segmentation of on-line images. Based on the previous methods for on-line image contouring such as registration-based contour adaptation and deep learning-based image segmentation, we proposed a technique that can combine the merits of individual method.
• Volumetric dose extension and isodose tuning. Interactive plan dose tuning is essential to on-line plan adaptation. We developed a dose painting algorithm that can output volumetric dose distribution from two isodose surfaces in real time. Then we applied the algorithm to interactive plan dose tuning, which allows for tuning volumetric dose by dragging isodose lines.
• Fluence map prediction. Plan optimization is the central part of inverse planning. We proposed a deep learning-based fluence map prediction method, to achieve inverse planning without optimization. Compared to fluence map optimization, fluence map prediction is as accurate and much faster because of its nature as a direct inference calculation.
• On-line proton range verification. Proton therapy is sensitive to motion and anatomy variation. For the previously proposed range-guided proton therapy strategy, we conducted an end-to-end Monte-Carlo simulation of the on-line proton measurement process. The key parameters of range measurement, such as the mapping from measurement result to range and measurement uncertainty, are obtained by simulation. Clinical data has been used to train and evaluate each technique. The results show the feasibility, generalizability, as well as limitations of the developed techniques. Future directions for on-line planning were discussed.