Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system

Abstract

This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages accurate pre-treatment verifications on radiation delivery in external beam radiotherapy. To ameliorate the optical image that suffers from mirror glare and edge blurring caused by photon scattering, a deep learning model is proposed and trained to learn from an on-board electronic portal imaging device (EPID). Beyond the typical purposes of an on-board EPID, the developed system maintains independent measurement with co-planar detection ability by involving a cylindrical receptor. Three task-aware modules are integrated into the network design to enhance its robustness against the artifacts that exist in an EPID running at the cine mode for efficient image acquisition. The training data consists of various designed beam fields that were modulated via the multi-leaf collimator (MLC). Validation experiments are performed for five regular fields ranging from 2 × 2 cm2 to 10 × 10 cm2 and three clinical IMRT cases. The captured CRIS images are compared to the high-quality images collected from an EPID running at the integration-mode, in terms of gamma index and other typical similarity metrics. The mean 2%/2 mm gamma pass rate is 99.14% (range between 98.6% and 100%) and 97.1% (ranging between 96.3% and 97.9%), for the regular fields and IMRT cases, respectively. The CRIS is further applied as a tool for MLC leaf-end position verification. A rectangular field with introduced leaf displacement is designed, and the measurements using CRIS and EPID agree within 0.100 mm ± 0.072 mm with maximum of 0.292 mm. Coupled with its simple system design and low-cost nature, the technique promises to provide viable choice for routine quality assurance in radiation oncology practice.