Super-Resolution Model for High-Precision In Vivo Proton Range Verification Using a Stereo Gamma Camera: A Feasibility Study

The feasibility of a deep-learning-based super-resolution (SR) model to improve the fiducial marker-tracking accuracy of a stereo portable gamma camera (SPGC) system over the range of an in vivo proton beam was verified in a Monte-Carlo (MC) simulation using the Geometry and Tracking 4 (Geant4) package. The SPGC system is capable of measuring the three-dimensional (3D) position of excited gold markers by detecting proton-induced X-ray emissions (PIXEs) generated by the interactions between the gold marker and a proton beam. The SPGC system was modeled using Geant4 according to manufacturer's specifications. The original image (I_o) acquired by using the SPGC system, which was comprised of 32 × 32 arrays over an area of 104 × 104 mm², was subjected to resolution enhancement to produce an SR-enhanced image (I_SR) (128 × 128 arrays) through a fully trained SR model based on a convolutional neural network (CNN). In virtual experiments, two portable gamma cameras were positioned perpendicular to each other. Next, a pair of I_o's were acquired by detecting the radiations from the exited gold marker positioned in a water phantom. Then, the fully trained SR model improved the quality of the I_o's by converting those to I_SR's. The 3D position of the radiation source was calculated by using Anger logic and 3D vector calculations. Virtual experiments for in vivo proton range verification using the SPGC system were performed by irradiating to a gold marker in a water phantom with a proton beam. A gold marker was placed at five different positions along the Bragg curve of a 100.0-MeV proton beam, which had a range of 74.5 mm in water. The proton beam was irradiated to deliver 20.0 Gy to the gold marker when it was positioned at the center of the Bragg peak; then, the PIXEs were measured by using the SPGC system. When a gold marker was at a different position, it was irradiated with the same dose for a quantitative comparison. Then 3D position of the gold marker was calculated for the original image (I_o) and for the high-resolution image (I_SR) to compare the detection accuracy. The averaged root-mean-square errors of the five positions between the reference and calculation for I_o and I_SR were 9.127 mm and 3.991 mm, respectively. In conclusion, the feasibility of using a deep-learning SR model for improving the image resolution of I_o and therefore, the tracking accuracy of the SPGC system was validated in MC simulations. The SR model can be applicable to diverse areas of research using gamma camera.