Enhancing Real-Time Imaging for Radiotherapy: Leveraging Hyperparameter Tuning with PyTorch

Abstract

Proton beam therapy is an advanced form of cancer radiotherapy that uses high-energy proton beams to deliver precise and targeted radiation to tumors, mitigating unnecessary radiation exposure to surrounding healthy tissues. Utilizing real-time imaging of prompt gamma rays can enhance the effectiveness of this therapy. Compton cameras are proposed for this purpose, capturing prompt gamma rays emitted by proton beams as they traverse a patient’s body. However, the Compton camera’s non-zero time resolution results in simultaneous recording of interactions, causing reconstructed images to be noisy and lacking the necessary level of detail to effectively assess proton delivery for the patient. In an effort to address the challenges posed by the Compton camera’s resolution and its impact on image quality, machine learning techniques, such as recurrent neural networks, are employed to classify and refine the generated data. These advanced algorithms can effectively distinguish various interaction types and enhance the captured information, leading to more precise evaluations of proton delivery during the patient’s treatment. To achieve the objectives of enhancing data captured by the Compton camera, a PyTorch model was specifically designed. This decision was driven by PyTorch’s flexibility, powerful capabilities in handling sequential data, and enhanced GPU usage, accelerating the model’s computations and further optimizing the processing of large-scale data. The model successfully demonstrated faster training performance compared to previous approaches and achieves an overall fair accuracy with so far limited hyperparameter tuning, highlighting its effectiveness in advancing real-time imaging of prompt gamma rays for enhanced evaluation of proton delivery in cancer therapy.