Room: Stars at Night Ballroom 1
Purpose: Accurate dose calculation is of vital importance for proton therapy. Monte Carlo (MC) dose calculation is recognized as the most accurate method. Deep learning methods, due to their superior speed, can now improve dose calculation accuracy by converting the pencil beam dose computed by treatment planning system (TPS) to the mimicked MC dose. This work aims to achieve this goal by developing a deep learning model that can precisely predict MC dose from patient CT image and proton pencil beam dose for different types of cancer.
Methods: The proposed model is based on our newly developed hierarchically densely connected U-Net (HD U-Net) deep learning architecture. The model includes two input channels: the pencil beam dose and the patient CT image. Both the pencil beam dose and the patient CT image were normalized before used for training. A total number of 202 patients (76 prostate patients, 94 liver patients and 32 lung patients) were randomly chose for training/validation and testing. The accuracy of the model was evaluated by comparing mean square error (MSE) and gamma index between the predict and the true MC dose distributions.
Results: Using one NVIDIA Tesla K80 card, the trained model takes less than 5 seconds to predict the MC dose. Compared with pencil beam doses, MSE between the predict and the true MC doses are improved by at least 64% in most test cases. The gamma index results show good agreements between the predict and the true MC dose. The 3D gamma passing rates of all test sets with 1%/1 mm criterion are over 89%.
Conclusion: The developed model can accurately predict MC dose distributions from pencil beam dose distributions and patient CT image. This model can be an efficient and practical tool to improve the accuracy of proton dose calculation of TPS.