Open-Source Radiation Exposure Extraction Engine (RE3) with Patient-Specific Outlier Detection

Abstract

We present an open-source, picture archiving and communication system (PACS)-integrated radiation exposure extraction engine (RE3) that provides study-, series-, and slice-specific data for automated monitoring of computed tomography (CT) radiation exposure. RE3 was built using opensource components and seamlessly integrates with the PACS. RE3 calculations of dose length product (DLP) from the Digital imaging and communications in medicine (DICOM) headers showed high agreement (R 2 =0.99) with the vendor dose pages. For study-specific outlier detection, RE3 constructs robust, automatically updating multivariable regression models to predict DLP in the context of patient gender and age, scan length, waterequivalent diameter (D w ), and scanned body volume (SBV). As proof of concept, the model was trained on 811 CT chest, abdomen + pelvis (CAP) exams and 29 outliers were detected. The continuous variables used in the outlier detection model were scan length (R 2 =0.45), D w (R 2 =0.70), SBV (R 2 =0.80), and age (R 2 =0.01). The categorical variables were gender (male average 1182.7 ±26.3 and female 1047.1±26.9 mGy cm) and pediatric status (pediatric average 710.7±73.6 mGy cm and adult 1134.5±19.3 mGy cm).