A trial for EBT3 film without batch-specific calibration using a neural network

This note reports a trial to establish an ANN (artificial neural network) method applying to EBT3 films of different batches without batch-specific calibration. Based on Pytorch (Facebook, https://pytorch.org/), a feed-forward ANN model was built to convert the pixel values of scanned images from different batches into absorbed dose. Films from different batches exposed to x-ray doses were digitized in transmission mode on an Epson 11000XL scanner for training and testing. The calculated dose map of TPS (radiation therapy planning system) was used as a label (the desired output) for the ANN model. To verify the performance and generalization of the ANN method, a cross-validation experiment was performed. Using the trained ANN method, the scanned images were converted into absorbed dose maps, and the converted dose maps have good agreement with the calculated dose maps from TPS. For films irradiated via the sliding window mode, the MSEs (mean square errors) of the trained batches were less than [Formula: see text] and the MSEs of the tested batches were less than [Formula: see text]. For patient intensity-modulated radiotherapy (IMRT) films, the γ(3%, 3 mm) between the dose maps obtained from the trained films and TPS exceeded 97.5%. The γ(3%, 3 mm) between most of the dose maps obtained from the tested films and TPS exceeded 97.0%. This shows that it is feasible to establish a method for EBT3 films from certain batches to convert pixel values into an absorbed dose without batch-specific calibration, and the method can be applied to other cases.