Fast operating room scattered radiation calculation in X-ray guided interventions by using deep learning

Objective. Protecting medical personnel from the harmful effects of scattered ionizing radiation during X-ray-guided procedures is a critical concern. Due to the complex and invisible nature of X-rays, monitoring radiation exposure has been challenging. Existing real-time dosimeters have shown low accuracy and practical limitations. Approach. To address these challenges, this study introduces an innovative approach that combines Monte Carlo (MC) simulations and Deep Learning (DL) for realtime estimation of 3D scattered radiation in the operating room. The neural network was trained to map patient morphology and imaging parameters to radiation maps, allowing it to adapt to various clinical scenarios. Main results. The results demonstrate that the system showcases exceptional speed by efficiently computing 3D radiation maps in 11 ms using modern GPU (NVIDIA RTX 2080). Validation experiments confirmed the reliability of the predicted scatter maps, with a mean absolute percentage error (MAPE) of 10.97\% relative to MC simulations. When used to compute organ doses via voxelized-source simulations, the global average organ dose error was 8.2 ± 4.1\%. Significance. Therefore, the combination of MC simulations and DL provides a promising solution for enhancing the safety of medical personnel during X-ray-guided procedures.