Purpose: In this study Artificial Neural Networks (ANN) are trained to develop a fast CT based dose calculation method. The ANN is capable of calculating the 3D dose distribution inside a voxel‐based phantom for prostate I–125 seed implants. The speed allows for accurate CT‐based calculation of the dose distributions during implantation in real time due to plan modifications. Methods: Training the ANN needs a set of accurate sample dose distributions in heterogeneous CT based phantoms. For the purpose of this study, 60000 sample cubicle phantoms were extracted from 3 patient CT studies. The dose distributions of the cubicle phantoms were calculated using MCNP5. The absorption coefficient of each voxel of the cubicle phantom was calculated by analyzing the CT scan. This information was then given to the ANN as the input. The calculated dose by MCNP5 was given to it as the sample output for the purpose of training. The dose distributions calculated by ANNs were then tested for 7000 cubicle phantoms against the MCNP5 results. Finally, the ANN was used to calculate the dose distribution in two real treatment plans. The results were compared against the MCNP5 voxel based results. Results: The regression graphs for individual ANNs show a regression of R=0.99543 for the worst ANN The regression of the best Trained ANN was equal to R=0.9997 for the 7000 cases. The maximum average absolute error of the ANN dose calculation was 3% in comparison with MCNP5. Conclusion: The results of individual ANN calculations and the complete treatment plan calculations verified the accuracy of the ANN method. Capability of the ANN method to calculate the dose distributions for 500 seed positions in less than 10 minutes suggests that this method can be used for model based dose calculation for real‐time plan modifications in the case of LDR brachytherapy.
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging