Promising Hyperparameter Configurations for Deep Fully Connected Neural Networks to Improve Image Reconstruction in Proton Radiotherapy

Abstract

Proton therapy is a unique form of radiotherapy that utilizes protons to treat cancer by irradiating cancerous tumors while avoiding unnecessary radiation exposure to surrounding healthy tissues. Real-time imaging of prompt gamma rays can be used as a tool to make this form of therapy more effective. The use of Compton cameras is one proposed method for the real-time imaging of prompt gamma rays that are emitted by the proton beams as they travel through a patient’s body. The non-zero time resolution of the Compton camera, during which all interactions are recorded as occurring simultaneously, causes the reconstructed images to be noisy and insufficiently detailed to evaluate the proton delivery for the patient. Deep Learning has been a promising method used to remove and correct the different problems existing within the Compton Camera’s data. Previous papers have demonstrated the effectiveness of using deep fully connected networks to correct improperly ordered gamma interactions within the data. We do a moderately large hyperparameter grid search to find a promising set which yields competitive performance but contains fewer neurons making it compact. The studies which have many neurons, many layers, and a non-zero dropout rate have the best testing accuracy. These many neuron and many layer networks still have significantly fewer total neurons than the current neural network implementation. If given considerably more training time these compact networks could yield equal, if not superior, testing accuracy when compared to larger networks. More improvements are still needed for clinical use and we are currently experimenting with recurrent neural networks to test the viability of this type of architecture for this application.