4D Pencil Beam Treatment Plan with Conditional Weight Predictions
TL;DR
The results suggest that extending this approach to full treatment prediction is feasible but requires further investigation, and the acceleration of 4D treatment generation via pencil beam weight prediction takes us one step closer to the feasibility of treating mobile targets with simplified, gantry-less and magnet-free scanner designs.
Abstract
Objective. This work aims to accelerate our four-dimensional (4D) proton pencil beam delivery strategy, which incorporates respiratory motion into a dynamic treatment plan, to improve dose conformity and treatment efficiency for gantry-less and magnet-free scanner designs. Approach. To accelerate the generation and adaptation of 4D treatment plans, we propose a conditional hybrid ResNet18-Transformer model for predicting pencil beam weights. The model predicts either the full set of pencil beam weights or a subset thereof. Main Results. The conditional model can successfully predict the remaining beam weights of a treatment, and the results suggest that extending this approach to full treatment prediction is feasible but requires further investigation. Significance. The acceleration of 4D treatment generation via pencil beam weight prediction takes us one step closer to the feasibility of treating mobile targets with simplified, gantry-less, and magnet-free scanner designs. Reducing system complexity while preserving dosimetric conformity may offer a pathway toward more accessible and cost-effective proton beam therapy for motion-affected tumours. Our code is available here: https://github.com/NairVonMuehlenen/Pencil-Beam-Weight-Prediction