@article {1067,
	title = {Open-Source Radiation Exposure Extraction Engine (RE3) with Patient-Specific Outlier Detection.},
	journal = {J Digit Imaging},
	year = {2015},
	month = {2015 Dec 7},
	abstract = {<p>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 {\textpm} 26.3 and female 1047.1 {\textpm} 26.9~mGy~cm) and pediatric status (pediatric average 710.7 {\textpm} 73.6~mGy~cm and adult 1134.5 {\textpm} 19.3~mGy~cm).</p>},
	issn = {1618-727X},
	doi = {10.1007/s10278-015-9852-y},
	author = {Weisenthal, Samuel J and Folio, Les and Kovacs, William and Seff, Ari and Derderian, Vana and Summers, Ronald M and Yao, Jianhua}
}
