Towards On-line Treatment Verification Using cine EPID for Hypofractionated Lung Radiotherapy

We propose a novel approach for on-line treatment verification using cine EPID (electronic portal imaging device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated lung radiotherapy has high precision requirement, and it is essential to effectively monitor the target making sure the tumor is within beam aperture. We model the treatment verification problem as a two-class classification problem and apply artificial neural network (ANN) to classify the cine EPID images acquired during the treatment into corresponding classes-tumor inside or outside of the beam aperture. Training samples of ANN are generated using digitally reconstructed radiograph (DRR) with artificially added shifts in tumor location-to simulate cine EPID images with different tumor locations. Principal component analysis (PCA) is used to reduce the dimensionality of the training samples and cine EPID images acquired during the treatment. The proposed treatment verification algorithm has been tested on six hypofrationated lung patients in a retrospective fashion. On average, our proposed algorithm achieved 94.66% classification accuracy, 94.50% recall rate, and 99.79% precision rate.