Using a convolutional neural network for human recognition in a staff dose management software for fluoroscopic interventional procedures

A Prototype Software System for Intra-procedural Staff Dose Monitoring and Virtual Reality Training for Fluoroscopically Guided Interventional Procedures

Staff dose management in fluoroscopically guided interventional procedures is a continuing problem. The scattered radiation display system (SDS), which our group has developed, provides in-room visual feedback of scatter dose to staff members during fluoroscopically guided interventional (FGI) procedures as well as extra-procedure staff and resident training. There have been a number of virtual safety training systems developed that provide detailed feedback for staff, but utilize expensive graphics processing units (GPUs) and dosimeter systems, or interaction with the x-ray system in a manner which entails additional radiation exposure and is not compatible with the As Low as Reasonably Achievable paradigm. The SDS, on the other hand, incorporates a library of look-up-table (LUT) room scatter distributions determined using the EGSnrc Monte Carlo software, which facilitates accurate and rapid system update without the need for GPUs. Real-time display of these distributions is provided for feedback to staff during a procedure. After a procedure is completed, machine parameter and staff position log files are stored, retaining all of the exposure and geometric information for future review. A graphic user interface (GUI) in Unity3D enables procedure playback and interactive virtual-reality (VR) staff and resident training with virtual control of exposure conditions using an Oculus headset and controller. Improved staff and resident awareness using this system should lead to increased safety and reduced occupational dose.

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.