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

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 open-source 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, water-equivalent 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).