Comparison of deep learning models for building two-dimensional non-transit EPID Dosimetry on Varian Halcyon

Abstract

Background

This study compared the effectiveness of five deep learning models in constructing non-transit dosimetry with an a-Si electronic portal imaging device (EPID) on Varian Halcyon. Deep learning model is increasingly used to support prediction and decision-making in several fields including oncology and radiotherapy.

Materials and methods

Forty-seven unique plans of data obtained from breast cancer patients were calculated using Eclipse treatment planning system (TPS) and extracted from DICOM format as the ground truth. Varian Halcyon was then used to irradiate the a-Si 1200 EPID detector without an attenuator. The EPID and TPS images were augmented and divided randomly into two groups of equal sizes to distinguish the validation and training–test data. Five different deep learning models were then created and validated using a gamma index of 3%/3 mm.

Results

Four models successfully improved the similarity of the EPID images and the TPS-generated planned dose images. Meanwhile, the mismatch of the constituent components and number of parameters could cause the models to produce wrong results. The average gamma pass rates were 90.07 ± 4.96% for A-model, 77.42 ± 7.18% for B-model, 79.60 ± 6.56% for C-model, 80.21 ± 5.88% for D-model, and 80.47 ± 5.98% for E-model.

Conclusion

The deep learning model is proven to run fast and can increase the similarity of EPID images with TPS images to build non-transit dosimetry. However, more cases are needed to validate this model before being used in clinical activities.