Estimating Compton scatter distributions with a regressional neural network for use in a real-time staff dose management system for fluoroscopic procedures.

Staff-dose management in fluoroscopic procedures is a continuing concern due to insufficient awareness of radiation dose levels. To maintain dose as low as reasonably achievable (ALARA), we have developed a software system capable of monitoring the procedure room scattered radiation and the dose to staff members in real-time during fluoroscopic procedures. The scattered-radiation display system (SDS) acquires imaging-system signal inputs to update technique and geometric parameters used to provide a color-coded mapping of room scatter. We have calculated a discrete look-up-table (LUT) of scatter distributions using Monte-Carlo (MC) software and developed an interpolation technique for the multiple parameters known to alter the spatial shape of the distribution. However, the file size for the LUT's can be large (~2GB), leading to long SDS installation times in the clinic. Instead, this work investigated the speed and accuracy of a regressional neural network (RNN) that we developed for predicting the scatter distribution from imaging-system inputs without the need for the LUT and interpolation. This method greatly reduces installation time while maintaining real-time performance. Results using error maps derived from the structural similarity index indicate high visual accuracy of predicted matrices when compared to the MC-calculated distributions. Dose error is also acceptable with a matrix element-averaged percent error of 31%. This dose-monitoring system for staff members can lead to improved radiation safety due to immediate visual feedback of high-dose regions in the room during the procedure as well as enhanced reporting of individual doses post-procedure.