title: Automated detection of changes in patient exposure in digital projection radiography using exposure index from DICOM header metadata.
abstract: OBJECTIVE
  Automated collection of image data from DICOM headers enables monitoring of patient dose and image quality parameters. Manual monitoring is time consuming, owing to the large number of exposure scenarios, thus automated methods for monitoring needs to be investigated. The aim of the present work was to develop and optimise such a method.
  
  
  METHODS
  Exposure index values from digital systems in projection radiography were collected over a period of five years, representing data from 1.2 million projection images. The exposure index values were converted to detector dose and an automated method for detection of sustained level shifts in the resulting detector dose time series was applied using the statistical analysis tool R. The method combined handling of outliers, filtering and estimation of variation in combination with two different statistical rank tests for level shift detection. A set of 304 time series representing central body parts was selected and the level shift detection method was optimised using level shifts identified by ocular evaluation as the gold standard.
  
  
  RESULTS
  Two hundred and eighty-one level changes were identified that were deemed in need of further investigation. The majority of these changes were abrupt. The sensitivity and specificity of the optimised and automated detection method concerning the ocular evaluation were 0.870 and 0.997, respectively, for detected abrupt changes.
  
  
  CONCLUSIONS
  An automated analysis of exposure index values, with the purpose of detecting changes in exposure, can be performed using the R software in combination with a DICOM header metadata repository containing the exposure index values from the images. The routine described has good sensitivity and acceptable specificity for a wide range of central body part projections and can be optimised for more specialised purposes.
authors.affiliation: Department of Radiology, Oncology and Radiation Science, Uppsala University, Uppsala, Sweden. Hans-Erik.Kallman@ltdalarna.se
authors.forename: Hans-Erik
authors.initials: HE
authors.lastname: Källman
authors.forename: Erik
authors.initials: E
authors.lastname: Halsius
authors.forename: Mikael
authors.initials: M
authors.lastname: Folkesson
authors.forename: Ylva
authors.initials: Y
authors.lastname: Larsson
authors.forename: Mats
authors.initials: M
authors.lastname: Stenström
authors.forename: Magnus
authors.initials: M
authors.lastname: Båth
mesh_terms: D001331:Automation; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D046609:Libraries, Digital; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D019047:Phantoms, Imaging; D011834:Radiation Monitoring; D011856:Radiographic Image Enhancement; D012680:Sensitivity and Specificity; D012984:Software