PURPOSE: 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. MATERIAL AND 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.
PURPOSE: 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. MATERIAL AND 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.