Farshad Safavi | Searcharxiv

arXiv2026

The First Nozzle-Mounted Compton Camera Prompt Gamma Imaging System for In Vivo Proton Therapy Dose Verification

This study presents the first clinical integration and experimental demonstration of a nozzle-mounted Compton camera prompt gamma imaging (PGI) system for in vivo proton range verification. Four position-sensitive solid-state Compton camera modules, each containing four cadmium zinc telluride (CdZnTe) detector crystals, were integrated into a modified range shifter mounted directly on the treatment nozzle of a clinical proton therapy gantry. This compact fixed-geometry configuration maintained alignment with the proton beam axis throughout irradiation and enabled stable synchronized data acquisition during pencil-beam scanning delivery. The system was evaluated under realistic clinical proton beam delivery conditions using single-energy and spread-out Bragg peak (SOBP) irradiations at gantry angles of 90{\deg} and 270{\deg}, delivered doses of 2 Gy and 7.5 Gy, and controlled distal range shifts of up to 10 mm. Prompt gamma events were reconstructed into three-dimensional emission distributions using a physics-based Compton scatter reconstruction framework. The system operated reliably during all irradiations and produced reproducible prompt-gamma localization across repeated measurements. Reconstructed emission distributions remained geometrically consistent across gantry angles and demonstrated sensitivity to controlled distal range perturbations, with measurable upstream shifts of the emission hotspot corresponding to reduced proton penetration depth. These results demonstrate the feasibility of a clinically integrated nozzle-mounted quad-camera Compton PGI system for detecting millimeter-scale proton range variations during beam delivery and represent an important step toward clinically deployable prompt gamma-based in vivo treatment verification in proton therapy.

physics.med-ph↗

arXiv2023

Efficient Semantic Segmentation on Edge Devices

Semantic segmentation works on the computer vision algorithm for assigning each pixel of an image into a class. The task of semantic segmentation should be performed with both accuracy and efficiency. Most of the existing deep FCNs yield to heavy computations and these networks are very power hungry, unsuitable for real-time applications on portable devices. This project analyzes current semantic segmentation models to explore the feasibility of applying these models for emergency response during catastrophic events. We compare the performance of real-time semantic segmentation models with non-real-time counterparts constrained by aerial images under oppositional settings. Furthermore, we train several models on the Flood-Net dataset, containing UAV images captured after Hurricane Harvey, and benchmark their execution on special classes such as flooded buildings vs. non-flooded buildings or flooded roads vs. non-flooded roads. In this project, we developed a real-time UNet based model and deployed that network on Jetson AGX Xavier module.

cs.CV↗