Implementing beam shape variations in a deep learning based proton dose calculation algorithm

Abstract

In this report, a transformer based deep learning proton dose calculation algorithm called Dose Transformer Algorithm (DoTA) is described. This model learns to predict proton dose distributions by being trained with Monte Carlo generated data. Monte Carlo is the golden standard of proton dose calculation because it is very accurate, but it has relatively long computation times. In current proton therapy treatment programmes, Monte Carlo algorithms are the most commonly used models to perform dose calculation. The goal of the DoTA model is to predict proton dose distributions with Monte Carlo accuracy in the fraction of the computation time of Monte Carlo algorithms to speed up the dose calculation process in proton therapy treatment. The DoTA model can take patient geometry, random proton beam energy and random proton beam shape (2D Gaussian with different major and minor axes) as input. The addition of the random proton beam shape input is discussed in this report, together with a detailed explanation of the DoTA model. The DoTA model is trained with data from 9 lung cancer patients and 9 head & neck cancer patients. Being used on an Intel(R) Core(TM) i7-8565U CPU, the DoTA model managed to produce results with a gamma pass rate of 98.45 ± 2.60 % with an average computation time of 0.3 seconds. The gamma pass rate determines how similar the DoTA predicted dose is to the reference (Monte Carlo generated) dose. Compared to the average computation time of the Monte Carlo algorithm that was used to generate the training data, which is 20 seconds on the same CPU, we can conclude that the DoTA model has the potential to greatly improve dose calculation times in proton therapy treatment, especially when used on a system with greater processing power. Because the DoTA model is able to deliver accurate results in a small amount of time, it has the potential to be used for real-time dose calculation. Real-time dose calculation could account for small changes in patient geometry during treatment, which increases the accuracy of the treatment and minimizes side effects. The DoTA model can also be used for other radiotherapy types like phonon therapy and electron therapy (in that case it needs to be trained with
different data).