Machine Learning-based beam delivery time model for Mevion 250i with Hyperscan technology

Abstract

Purpose: Accurate prediction of beam delivery time (BDT) is essential for operational efficiency, 4D dose calculations, and advanced proton therapy techniques. Despite its importance, no machine-specific BDT model exists for Mevion systems. Methods: We developed the first machine learning-based BDT model for the Mevion S250i Hyperscan system. Institutional machine log files from 11 patients (1120 files) were used to extract features including spot position, energy layer changes, Adaptive Aperture (AA) movements, and spot charge. Inter-pulse time ($\Delta$T) was the target variable. A Random Forest model was trained with cross-validation and tested on held-out data. SHAP (Shapley Additive Explanations) analysis was used to quantify feature contributions. Results: The model achieved mean absolute errors (MAE) ranging from 0.9 ms for short intervals (<50 ms) to 222 ms for long delays (>1000 ms). AA movements were the dominant global predictor for $\Delta$T > 50 ms, while spot positions and pulse charge influenced short intervals. Energy changes had minor global impact but locally contributed to large $\Delta$T values, consistent with range modulator physics. The model was tested in two clinical applications: volumetric repainting and 4D dose recalculation for interplay evaluation. Predicted cumulative delivery times deviated by only -1.7% from machine log data, and dosimetric metrics (D98, D95, V95) remained within intrinsic delivery variability. Conclusions: This study presents the first machine-specific BDT model for the Mevion S250i, accurately capturing temporal dynamics and predictive performance. SHAP analysis provided insight into system behavior, highlighting the roles of AA adjustments, energy switching, and spot positioning. The model supports applications in interplay assessment, 4D dose calculation, and delivery time-based plan optimization.