Speaker: Ziyuan Wang
Title: Automatic radiotherapy plan emulation for 3D dose reconstruction to enable big data analysis for historically treated patients
Abstract: 3D Dose Reconstruction (DR) for radiotherapy (RT) is the estimation of the 3D radiation dose distribution patients received during RT. Big DR data is needed to accurately model the relationship between the dose and onset of adverse effects, to ultimately gain insights and improve today’s treatments.
DR is often performed by emulating the original RT plan on a surrogate anatomy for dose estimation. This is especially essential for historically treated patients with long-term follow-up, as solely 2D radiographs were used for RT planning, and no 3D imaging was acquired for these patients. Performing DRs for a large group of patients requires large amount of manual work, where the geometry of the original RT plan is emulated on the surrogate anatomy, by visually comparing the latter with the original 2D radiograph of the patient. This is a labor-intensive process that for practical use needs to be automated.
This work presents an image-processing pipeline to automatically emulate plans on surrogate CTs. The pipeline was designed for pediatric cancer survivors that historically received abdominal RT with anterior-to-posterior and posterior-to-anterior RT field set-up. First, anatomical landmarks are automatically identified on 2D radiographs. Next, these landmarks are used to derive parameters needed to finally emulate the plan on a surrogate CT. Validation was performed by an experienced RT planner, blindly visually assessing 12 cases of automatic and manual plan emulations. Automatic emultions were approved 11 out of 12 times. This work paves the way to effortless scaling of DR data generation.
