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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-08

AUTHORS

Samuel J. Weisenthal, Les Folio, William Kovacs, Ari Seff, Vana Derderian, Ronald M. Summers, Jianhua Yao

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 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). More... »

PAGES

406-419

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10278-015-9852-y

DOI

http://dx.doi.org/10.1007/s10278-015-9852-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002338651

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26644157


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