  In this study we performed a feasibility investigation on implementing a fast
and accurate dose calculation based on a deep learning technique. A two
dimensional (2D) fluence map was first converted into a three dimensional (3D)
volume using ray traversal algorithm. A 3D U-Net like deep residual network was
then established to learn a mapping between this converted 3D volume, CT and 3D
dose distribution. Therefore an indirect relationship was built between a
fluence map and its corresponding 3D dose distribution without using
significantly complex neural networks. 200 patients, including nasopharyngeal,
lung, rectum and breast cancer cases, were collected and applied to train the
proposed network. Additional 47 patients were randomly selected to evaluate the
accuracy of the proposed method through comparing dose distributions, dose
volume histograms (DVH) and clinical indices with the results from a treatment
planning system (TPS), which was used as the ground truth in this study.
Results: The proposed deep learning based dose calculation algorithm achieved
good predictive performance. For 47 tested patients, the average per-voxel bias
of the deep learning calculated value and standard deviation (normalized to the
prescription), relative to the TPS calculation, is 0.17%. The average deep
learning calculated values and standard deviations for relevant clinical
indices were compared with the TPS calculated results and the t-test p-values
demonstrated the consistency between them. Conclusions: In this study we
developed a new deep learning based dose calculation method. This approach was
evaluated by the clinical cases with different sites. Our results demonstrated
its feasibility and reliability and indicated its great potential to improve
the efficiency and accuracy of radiation dose calculation for different
treatment modalities
