Abstract
Reject analysis fosters a culture of continuous accountability and improvement in
Radiography. Reject analysis highlights the important role radiographers play in evaluating
images and ensuring the acceptability of high-quality images. Radiographers’ skills and
professional judgement are often tested to minimise the number of rejected images
accumulated and increase the rate of high-quality diagnostic images. Globally, common
reasons for rejected images in digital radiography include positioning errors, anatomy cut-off,
improper collimation and misplaced or absent lead side markers. Digital radiography brings
advancements in technology, yet challenges, such as rejected images, persist. Thus, this
study investigates reject analysis in digital radiography within Gauteng, South Africa.
A quantitative, descriptive study was conducted over two phases. Phase one retrospectively
analysed reject analysis data from three hospitals across three non-consecutive months
(January, March and June 2024) to calculate overall reject rates, identify the most frequently
rejected anatomical regions and determine common reasons for image rejection. In phase
two, radiographers’ attitudes toward reject analysis were assessed using a structured
questionnaire. Descriptive and inferential statistics were applied to interpret the results.
Results revealed that in phase one, reject rates across the three hospitals fell within or slightly
above the internationally acceptable range of 5–10%; the average reject rate across all three
hospitals was 7.54%, with patient positioning and anatomy cut-off being the most common
reasons for image rejection. Chest and lower limb anatomical regions were recorded as the
most frequently rejected images across all three hospitals. The total rejected images across
hospitals A, B and C were 2354 images. Hospital A’s reject rates ranged from 7.50%, 4.99%
and 6.32% over the recorded months (January, March & June). A total of 699 rejected images
were recorded. Hospital B’s reject rate ranged from 8.82%, 6.75% and 9.76% for the
respective months. A total of 605 rejected images were recorded. Hospital C’s reject rates
ranged from 9.55%, 7.20% and 8.28% for January, March and June 2024, respectively. A total
of 1050 rejected images were recorded. In phase two, the questionnaire received a response
rate of 92.16% (n=94). Phase two demonstrated that radiographers recognised the importance
of reject analysis in improving image quality and patient safety; however, gaps were identified
in formal training and departmental feedback. Most respondents indicated that additional
training in reject analysis would be beneficial. The results highlight the need for structured
reject analysis feedback to radiographers within the radiology departments and
encouragement for continuous professional development. Implementing these measures may
contribute to improved image quality and reduced patient radiation exposure within South
African radiology departments.