Optimizing Patient Radiation Safety in Medical Imaging Using Opdosr-Based Control within the AEC Framework

Abstract

Because diagnostic X-ray imaging is used so often, radiation safety standards need to be constantly improved to follow the ALARA (As Low as Reasonably Achievable) principle while yet keeping image quality.  Traditional Automatic Exposure Control (AEC) systems, although essential for modifying exposure parameters, depend on static heuristics, resulting in ineffective dose utilization and a failure to adapt to anatomical differences.  This study investigates the use of the Opdosr algorithm as a data-driven, dynamic optimization model inside the AEC framework to improve radiation safety and efficacy in imaging.  We evaluated the efficacy of standard AEC and the Opdosr enhanced AEC system using phantom-based simulations with polymethyl methacrylate (PMMA) slabs ranging from 10 to 25 cm in thickness.  The findings indicated a significant reduction in radiation dosage across all phantom thicknesses (22.725, p<0.05), while quality metrics for crucial images showed enhancements, with the contrast-to-noise ratio (CNR) increasing by 5.6 and spatial resolution by 5.3.  The Opdosr algorithm was quite realistic.  Optimization required an average of 35 ms, had a convergence rate of 98 percent, and could work with a broad variety of imaging settings.  These findings show that the AEC system based on Opdosr can effectively change the exposure settings in real time. This significantly decreases the radiation exposure for patients and improves the quality of diagnostic images.   This technology is a step in the right direction since it makes radiology procedures smarter and more flexible, which makes them safer and more accurate