Validation of cone beam tomographic images generated by deep learning neural networks for adaptive Radiotherapy
DOI:
https://doi.org/10.37004/sefm/2024.25.1.003Keywords:
Synthetic CT, neural networks, U-NET, adaptive RadiotherapyAbstract
During radiotherapy treatments, tomographic images of the patient are acquired. There is interest in using those images for treatment re-planning. LINAC images, in general, have artifacts that limit their validity for dose calculation. The use of neural networks can enhance the image quality to make them more apt for dose calculation. In this work, we use the well-known neural network U-NET to train a model capable of generating tomographic images valid for dose calculation. The model is trained with images of thorax of 25 patients. The result is verified over the images of 14 patients: 10 thorax and 4 pelvis. The CBCT images are used to generate synthetic CT’s which are compared with the simulation ones. Mean error, mean absolute error, maximum signal to noise ratio and structural similarity index are evaluated.
Doses calculated on both CBCT images and CT are compared. Evaluated parameters are gamma index, D5, D95 and mean dose in the dose volume histogram of the target volume.
All the parameters evaluated presented a statistically relevant improvement.
U-NET neural network can be used on CBCT images to make them more apt for dose calculation.
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