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Clinical DICOM Header Analytics for Practice Quality Improvement and Equipment Utilization in Digital Radiography

Z Long*, A Walz-Flannigan , S Stekel , B Stuve, L Littrell, B Schueler , Mayo Clinic, Rochester, MN


(Monday, 7/15/2019) 3:45 PM - 4:15 PM

Room: Exhibit Hall | Forum 8

Purpose: DICOM header contains a wealth of information about the exact image acquisition and processing parameters which could be affected by technologists and scanner configuration. This study aimed to extract and analyze header information of clinical images for practice quality improvement and equipment utilization in digital radiography (DR).

Methods: DICOM receiver was set up to receive images from 18 DR scanners. MATLAB programs were written to extract both public and private header elements. Besides study-related tags such as study time/description, station name and software version, data collection also includes acquisition-related tags such as kV, SID, exposure mode (i.e., AEC or manual), AEC chamber selection, mAs, exposure time, grid use/focal distance, collimator shape, detector serial number, exposure index (EI), EI target and deviation index, as well as processing-related tags such as processing name, window width/level, parameters such as contrast, brightness, edge enhancement when available.

Results: Data has been used for 1) Practice standardization. For example, grid was found to be left out for several table/wall exams. 10.9% lumbar spine exams were performed with manual technique. 12 exam views on vendor A showed different processing involving factory and custom settings; 2) Practice optimization such as EI target development. Even though scanners offer EI log from archive, this program allowed us to first filter out exams that did not appropriately follow technique charts to avoid distortion of the EI distribution. 3) Equipment utilization. For example, our practice wants to determine if Siemens 10’’×12’’ detectors were used for purchase consideration. Together with detector serial number inventory, 1074 images were found to be acquired with them in two months across multiple body parts.

Conclusion: Data analytics from clinical DICOM header provided us information on equipment, image acquisition and processing. These data have been utilized for technologist education, quality improvement and equipment utilization.


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