Technical note: Four‐year experience with utilization of DICOM metadata analytics in clinical digital radiography practice

Abstract

Background Digital radiography (DR) still presents many challenges and could have complex imaging acquisition and processing patterns in a clinical practice hindering quality standardization. Purpose This technical note aims to report the 4‐year experience with utilizing a custom DICOM metadata analytics program in clinical DR at a large institution. Methods Thirty‐eight DR systems of three vendors at multiple locations were configured to automatically send clinical DICOM images to a DICOM receiver. A suite of custom MATLAB programs was established to extract and store public and private header data weekly. Specific use cases are provided for systematic image acquisition investigation, image processing harmonization, exposure index (EI) longitudinal monitoring and EI target optimization. Results For systematic acquisition investigation, an example of adult lumbar spine exam analysis was provided with statistics on manual acquisition versus the use of automatic exposure control (AEC, including AEC dose level, active cell, and backup timer), grid usage, and collimation for various projections. For processing harmonization, up to 12.6% of protocols were revealed to have processing parameter differences in an example of a mobile radiography fleet. In addition, inconsistent use of a post‐acquisition image size function was also demonstrated, which resulted in anatomy size display variations. Bimonthly monitoring of median EI values showed expected trends, including changes after an AEC dose level adjustment for adult posterior‐anterior chest imaging on a scanner system. An example of adult axillary shoulder EI target refinement was shared using the median value, eμ, based on the lognormal EI data distribution after parsing down to acquisitions with appropriate techniques. Conclusions This analytics program enables systematic analysis of image acquisition and processing details. The information provides invaluable insights into real practice patterns, which can support data‐driven quality standardization and optimization.

Workload and use factor data for a modern digital radiography system

“…A custom DICOM analytics program was written in MATLAB (Math-Works, Natick, MA) to extract and store both public and private DICOM metadata. 9 Relevant DICOM tags (Table 1) were extracted, such as study date, presentation intent type, station name, peak kilovoltage (kVp), and milliampere-seconds (mAs).Station name was used to ensure inclusion of only the relevant DR scanners. Presentation intent type was used to differentiate for-processing and for-presentation images on GE systems, and only exposures tagged for-processing were included.…”

“…A Digital Imaging and Communications in Medicine (DICOM) receiver was set up to receive clinical images from all above‐mentioned scanners at the general hospital and pediatric practices, including DICOM for‐presentation images as well as for‐processing and rejected images whenever possible. A custom DICOM analytics program was written in MATLAB (MathWorks, Natick, MA) to extract and store both public and private DICOM metadata 9 . Relevant DICOM tags (Table 1) were extracted, such as study date, presentation intent type, station name, peak kilovoltage (kVp), and milliampere‐seconds (mAs).…”