Building a precise machine-specific time structure of the spot and energy delivery model for a cyclotron-based proton therapy system.
Document Type
Article
Publication Date
1-17-2022
Publication Title
Physics in medicine and biology
Abstract
Objective. We proposed an experimental approach to build a precise machine-specific beam delivery time (BDT) prediction and delivery sequence model for standard, volumetric, and layer repainting delivery based on a cyclotron accelerator system.Approach. Test fields and clinical treatment plans' log files were used to experimentally derive three main beam delivery parameters that impacted BDT: energy layer switching time (ELST), spot switching time, and spot drill time. This derived machine-specific model includes standard, volumetric, and layer repainting delivery sequences. A total of 103 clinical treatment fields were used to validate the model.Main results. The study found that ELST is not stochastic in this specific machine. Instead, it is actually the data transmission time or energy selection time, whichever takes longer. The validation showed that the accuracy of each component of the BDT matches well between machine log files and the model's prediction. The average total BDT was about (-0.74 ± 3.33)% difference compared to the actual treatment log files, which is improved from the current commercial proton therapy system's prediction (67.22%±26.19%).Significance. An accurate BDT prediction and delivery sequence model was established for an cyclotron-based proton therapy system IBA ProteusPLUS®. Most institutions could adopt this method to build a machine-specific model for their own proton system.
Volume
67
Issue
1
Recommended Citation
Zhao L, Liu G, Zheng W, Shen J, Lee A, Yan D, et al [Deraniyagala R, Stevens C, Li X, Ding X] Building a precise machine-specific time structure of the spot and energy delivery model for a cyclotron-based proton therapy system. Phys Med Biol. 2022 Jan 17;67(1). doi: 10.1088/1361-6560/ac431c. PMID: 34905732.
DOI
10.1088/1361-6560/ac431c
ISSN
1361-6560
PubMed ID
34905732