Deep Learning Based Classification Methods of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy

Abstract

Proton beam radiotherapy is a method of cancer treatment that uses proton beams to irradi ate cancerous tissue, while simultaneously sparing doses to healthy tissue. In order to optimize radiation doses to the tumor and ensure that healthy tissue is spared, many researchers have suggested verifying the treatment delivery through the use of real-time imaging. One promising method of real-time imaging is the use of a Compton camera, which can image prompt gamma rays that are emitted along the beam’s path through the patient. However, because of limita tions in the Compton camera’s ability to detect prompt gammas, the reconstructed images are often noisy and unusable for verifying proton treatment delivery. Machine learning is able to automatically learn patterns that exist in numerical data, making it a promising method to ana lyze Compton camera data for the purpose of reducing noise in the reconstructed images. First, we provide motivation for training deep neural networks over standard ensemble techniques. We then present the usage of supervised deep neural networks to detect and exploit these patterns so that we can remove and correct the various problems that exist within our data.