Determining Optimal Configurations for Deep Fully Connected Neural Networks to Improve Image Reconstruction in Proton Radiotherapy
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2021-12
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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 detected gamma interactions within the data.
More thorough hyperparameter studies than in previous works show that using a combination
of larger batch sizes, higher neurons per layer, and higher layer counts tend to produce better
performing networks, which show promise in reducing the complexity of previous network architectures. We also experiment with recurrent neural networks to test the viability of this type
of architecture and report initial results.