Nuclear Fragmentation Imaging for Monitoring: an In Silico Study

Purpose: This article presents an in vivo imaging technique based on nuclear fragmentation of carbon ions in irradiated tissues for potential real-time monitoring of carbon-ion radiation therapy (CIRT) treatment delivery and quality assurance purposes in clinical settings. Materials and Methods: A proof-of-concept imaging and monitoring system (IMS) was devised to implement the technique. Monte Carlo simulations were performed for a prospective pencil-beam scanning CIRT nozzle. The development IMS benchmark considered a 535-cm(2) pixelated charged-particle detector stack positioned downstream from a target phantom and list-mode data acquisition. The abundance and production origins, that is, vertices, of the detected fragments were studied. Fragment trajectories were approximated by straight lines and a beam back-projection algorithm was built to reconstruct the vertices. The spatial distribution of the vertices was then used to determine plan relevant markers. Results: The IMS technique was applied for a simulated CIRT case, a primary brain tumor. Four treatment plan monitoring markers were conclusively recovered: a depth dose distribution correlated profile, ion beam range, treatment target boundaries, and the beam spot position. Promising millimeter-scale (3-mm, <10% uncertainty) beam range and submillimeter (<0.6-mm precision for shifts ,3 cm) beam spot position verification accuracies were obtained for typical therapeutic energies between 150 and 290 MeV/u. Conclusions: This work demonstrated a viable online monitoring technique for CIRT treatment delivery. The method's strong advantage is that it requires few signal inputs (position and timing), which can be simultaneously acquired with readily available technology. Future investigations will probe the technique's applicability to motion-sensitive organ sites and patient tissue heterogeneities. In-beam measurements with candidate detector-acquisition systems are ultimately essential to validate the IMS benchmark performance and subsequent deployment in the clinic.