Proton therapy has potential for high precision dose delivery, provided that high accuracy is achieved in imaging. Currently, X-ray based techniques are preferred for imaging prior to proton therapy, and the stopping power conversion tables cause irreducible uncertainty. The proposed proton imaging methods aim to reduce this source of error, as well as lessen the radiation exposure of the patient. CARNA is a homogeneous compact calorimeter that utilizes a novel highdensity scintillating glass as an active medium. The compact design and unique geometry of the calorimeter eliminate the need for a tracker system and allow it to be directly attached to a gantry. Thus, giving CARNA potential to be used for insitu imaging during the hadron therapy, possibly to detect the prompt gammas. The novel glass development and the traditional image reconstruction studies performed with CARNA have been reported before. However, to improve the image reconstruction, a machine learning implementation with CARNA is reported. A proof-of-concept Artificial Neural Network, is shown to efficiently predict the density and the shape of the tumors.
In recent years, proton therapy has achieved remarkable precision in delivering doses to cancerous cells while avoiding healthy tissue. However, in order to utilize this high precision treatment, greater accuracy in patient positioning is needed. An accepted approximate uncertainty of ±3% exists in the current practice of proton therapy due to conversions between x-ray and proton stopping power. The use of protons in imaging would eliminate this source of error and lessen the radiation exposure of the patient. To this end, this study focuses on developing a novel proton-imaging detector built with high-density glass scintillator. The model described herein contains a compact homogeneous proton calorimeter composed of scintillating, high density glass as the active medium. The unique geometry of this detector allows for the measurement of both the position and residual energy of protons, eliminating the need for a separate set of position trackers in the system. Average position and energy of a pencil beam of 10<sup>6</sup> protons is used to reconstruct the image rather than by analyzing individual proton data. Simplicity and efficiency were major objectives in this model in order to present an imaging technique that is compact, cost-effective, and precise, as well as practical for a clinical setting with pencil-beam scanning proton therapy equipment. In this work, the development of novel high-density glass scintillator and the unique conceptual design of the imager are discussed; a proof-of-principle Monte Carlo simulation study is performed; preliminary two-dimensional images reconstructed from the Geant4 simulation are presented.