Treatment planning for radiation therapy is a complex process, as there are many machine parameters to determine for a treatment. To decrease the required labour and improve the plan quality, auto-planning systems have been developed, which can automatically generate high-quality treatment plans. These plans still have to be checked to ensure their quality, which means an independent and automatic quality assurance method is needed. This is where deep learning comes in, as recent advancements in computer hardware and artificial intelligence make it possible to quickly train neural networks for fast predictions of dose distributions based on the anatomy of a certain patient. Even though these models have proven capable of generating predictions comparable to the actual delivered dose, there is no guarantee that the distributions are actually deliverable. Therefore, this study is conducted to develop a novel deep learning model that uses physical information of how dose is delivered to constrain the neural networks and force realistic and deliverable dose distributions. The models presented in this study are implemented on prostate cancer patients treated with the Volumetric-Modulated Arc Therapy (VMAT) delivery technique.

First, the conventional Anatomy-based dose prediction model is further developed to improve the quality of the predicted dose distributions. The goal is to assess how accurate the predicted dose is from a deep learning model without any physical constraint. The model with U-net architecture has shown to be capable of predicting dose distributions of very high quality with average DVH differences of less than 1 Gy and errors in the dose coverage statistics on the PTV of less than 1\%. Furthermore, the lack of deliverability was confirmed due to the missing ray effects in the dose distributions, showing no clear entry points of the external photon beam. This lack of ray effects was quantified and confirmed using the dip in the S\o rensen-Dice Index around the 30\% isodose contours, where the ray effects are most apparent.

Second, the Physics-guided prediction model was developed by combining the Anatomy-based dose prediction model with the newly developed Segment prediction model, which predicts Multi-Leaf Collimator (MLC) positions and beam intensity values for all directions from which the dose is delivered. These predictions are based on the prediction from the Anatomy-based dose prediction model and the Beam's Eye View images of the CT and the delineated structures. The segment prediction is used in a stand-alone dose engine, based on the Matrad dose calculation algorithm, to calculate a dose distribution from the predicted treatment plan, which is deliverable by definition. The performance of the Physics-guided prediction model is underwhelming, scoring considerably worse on most evaluation metrics compared to the Anatomy-based dose prediction model. The lack of performance is mainly caused by the poor performance of the Segment prediction model component.

To circumvent the issue of the Segment prediction model, a dose mimicking model has been developed and investigated, where a dose mimicking algorithm is used to mimic a prediction from the Anatomy-based dose prediction model by optimizing the segments on which the dose is delivered. This model has only been tested on a single patient but seems to be performing better than the Physics-guided prediction model, with the downside that an optimisation process is involved. Still, the performance is not on par with the Anatomy-based dose prediction model.

Further investigation on the Segment prediction model will be needed before the Physics-guided prediction model can be considered as a serious quality assessment tool. Once performance of the Segment prediction model has increased, it could prove an interesting model that could even replace an auto-planning system in the far future.