Proton therapy is a great way to treat cancer, since protons can concentrate energy on one single spot, which minimises the irradiated healthy tissue. Because of this property protons are sensitive to uncertainties. Since the tumour is not stationary throughout the treatment, multiple scans are essential. With the current dose calculation technique (Monte Carlo simulations) this is not possible, since the simulations take too much time. This report therefore proposes a new deep learning algorithm to calculate accurate proton dose distributions much faster than the currently used methods.

The developed deep learning model consists of a convolutional encoder, two transformer encoder blocks and a convolutional decoder. Slices of a Computed Tomography (CT) image are processed through the model to output a corresponding dose distribution. The model is trained using gradient descent with a mean squared error loss function. The total dataset used to train, validate and test the model consists of 9,940 samples which are created using slices of a patient's CT scan and Monte Carlo simulations. Only a proton beam energy of 134 MeV is evaluated.

The model yielded a mean gamma-analysis index pass rate of 99.87 +/- 0.16 \%, which is much higher than any other model or method available. The model struggles most with predicting complex dose distributions but is excellent at predicting the general beam shape and the Bragg-peak location of the dose distribution. The average run-time of the model lies around 75 ms, which is much faster than Monte Carlo simulations and is even faster than the Pencil Beam method. The run-time is roughly equal to the fastest deep learning alternative when considering image dimensions.

For future researches it is suggested to train the model with more data to improve the accuracy, train the model with different proton beam energies to see if it generalises well and find the optimal convolutional encoder and decoder parameters to decrease run-time.