Background/introduction
Currently,
regular quality control (QC) for digital radiography (DR) modalities requires significant user interaction,
not only for acquiring the images,
but also for processing and interpreting the QC results.
This processing and interpretation may take up to 10-15 minutes per modality,
which is not only time consuming,
but may also lead to subjective,
user dependent QC outcomes.
To overcome these drawbacks,
we implemented automated QC for all DR modalities at our radiology department using a freely available open-source software platform.
Description of activity and work performed
Initial QC measurements were performed by two experienced radiology technologists according to a predefined protocol.
After a few testing and feedback sessions,
the protocol was refined and updated.
QC measurements were then scheduled through our Radiology Information System (RIS) on a monthly basis.
Measurements were performed for DR modalities from three different vendors: Philips Digital Diagnost,
Carestream DRX-Revolution and Konica-Minolta Aero-DR.
Images of a NORMI13 phantom (PTW-Freiburg GmbH) were obtained using the same exposure parameters (kV,
mAs,
filtering,
SID) on the different modalities.
The QC...
Conclusion and recommendations
We implemented automated QC for all DR modalities at our Radiology department using an open source QC software platform.
This resulted in a reduction of 10-15 minutes spent by the radiology technologist performing a QC measurement.
Also,
the QC procedure has been significantly simplified and now only consist of three steps: positioning of the phantom,
exposure of the phantom using a predefined protocol,
and sending the resulting image to a DICOM server.
The automated analysis of the images ensures reproducible results.
These results can be...
Personal/organisational information
E.
J.
Rijkhorst,
PhD,
Department of Medical Physics and Technology
H.
Huurdeman,
Department of Radiology
B.
Spil,
Department of Radiology
R.
G.
H.
Beets-Tan,
MD,
PhD,
Department of Radiology
The Netherlands Cancer Institute
References
1.
WAD-QC web-site: github.com/wadqc
2.
WAD-QC 2.0 python web-site: bitbucket.org/rvrooij/pywad3
3.
S.
Jodogne,
Orthanc: open-source lightweight DICOM server,
www.orthanc-server.com
4.
WAD-QC analysis module for NORMI13 phantom: github.com/wadqc:Plugins/Bucky/Normi13
5.
Van Horssen et al.,
Automated quality control of ultrasound based on in-air reverberation patterns,
Ultrasound,
vol 25,
issue 4,
p229-238,
2017