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Background and purpose: To propose a novel machine learning-based method forreliable and accurate modeling of linac beam data applicable to the processesof linac commissioning and QA. Materials and methods: We hypothesize that thebeam data is a function of inherent linac features and percentage depth doses(PDDs) and profiles of different field sizes are correlated with each other.The correlation is formulated as a multivariable regression problem using amachine learning framework. Varian TrueBeam beam data sets (n=43) acquired frommultiple institutions were used to evaluate the framework. The data setsincluded PDDs and profiles across different energies and field sizes. Amultivariate regression model was trained for prediction of beam specific PDDsand profiles of different field sizes using a 10x10cm$^2$ field as input.Results: Predictions of PDDs were achieved with a mean absolute percentrelative error (%RE) of 0.19-0.35% across the different beam energiesinvestigated. The maximum mean absolute %RE was 0.93%. For profile prediction,the mean absolute %RE was 0.66-0.93% with a maximum absolute %RE of 3.76%. Thelargest uncertainties in the PDD and profile predictions were found at thebuild-up region and at the field penumbra, respectively. The predictionaccuracy increased with the number of training sets up to around 20 trainingsets. Conclusions: Through this novel machine learning-based method we haveshown accurate and reproducible generation of beam data for linac commissioningfor routine radiation therapy. This method has the potential to simplify thelinac commissioning procedure, save time and manpower while increasing theaccuracy of the commissioning process.

Beam data modeling of linear accelerators (linacs) through machine  learning and its potential applications in fast and robust linac  commissioning and quality assurance