ArticleJournal of medical radiation sciences2020
Reject rate analysis in digital radiography: an Australian emergency imaging department case study.
Article in Journal of medical radiation sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Building Research Capacity in Medical Radiation Sciences Through University-Clinical Partnerships: A Commentary on Working Smarter, Not Harder.Journal of medical radiation sciences · 2026Article
- Towards Automated Quality Assurance: Integrating Deep Learning and Classical ML into the Digital Radiography Pipeline.Diagnostics (Basel, Switzerland) · 2026Article
- Application of multi-scale feature extraction and explainable machine learning in chest x-ray position evaluation within an integrated learning framework.European radiology · 2026Article
- Image Reject Patterns in Computed Radiography: Insights From a Ghanaian Radiology Department.Journal of medical radiation sciences · 2026Article
- Quality control system for patient positioning and filling in meta-information for chest X-ray examinations.International journal of computer assisted radiology and surgery · 2025Article
- Positional Challenges of the Anteroposterior Pelvic X-Ray: Comparison of Imaging Reject Rates Between Trauma Trolley and Table Bucky.Journal of medical radiation sciences · 2025Article
- Multi-centre digital radiography reject analysis for different clinical room use types: The establishment of local reject reference levels for public hospital departments.Journal of medical radiation sciences · 2024Article
- The cost of perfection: An investigation into the unnecessary rejection of clinically acceptable lateral wrist imaging.Journal of medical radiation sciences · 2023Review
- Making the most of X-ray reject analysis in the digital age.Journal of medical radiation sciences · 2023Article
- Understanding reasons for image rejection by radiologists and radiographers.Journal of medical radiation sciences · 2023Article
- Digital radiography reject analysis: A comparison between two radiology departments in New Zealand.Journal of medical radiation sciences · 2023Article
- Knee positioning systems for X-ray environment: a literature review.Physical and engineering sciences in medicine · 2023Review
- Do radiographers collimate? A retrospective analysis of radiographic collimation of common musculoskeletal examinations at an adult trauma centre.Journal of medical radiation sciences · 2023Article
- Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution.Diagnostics (Basel, Switzerland) · 2023Review
- The assessment of image quality and diagnostic value in X-ray images: a survey on radiographers' reasons for rejecting images.Insights into imaging · 2022Article
- MRI interobserver reliability in rectal tumor angulation.Acta radiologica open · 2022Article
- How has the COVID-19 pandemic affect the rejection rates and repeat reasoning of digital radiography?Saudi medical journal · 2021Article
- Digital radiography reject analysis of examinations with multiple rejects: an Australian emergency imaging department clinical audit.Journal of medical radiation sciences · 2021Article
- Bridging the communication gap between radiographers and patients to improve chest radiography image acquisition: A multilingual solution in the COVID-19 pandemic.Radiography (London, England : 1995) · 2021Article
- Optimisation in daily practice - it's more than just radiation dose.Journal of medical radiation sciences · 2020Article
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3 authors.
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Abstract
introductionReject analysis in digital radiography (DR) helps guide the education and training of staff, influences department workflow, reduces patient dose and improves department efficiency. The purpose of this study was to investigate rejected radiographs at a major metropolitan emergency imaging department to help form a benchmark of reject rates for DR and to assess what radiographs are being rejected and why.
methodsA retrospective longitudinal study was undertaken as an in-depth clinical audit. The data were collected using automated reject analysis software from two digital x-ray systems from June 2015 to April 2017. The overall reject rate, reasons for rejection as well as the reject rates for individual radiographers, examination types and projections were analysed.
resultsA total of 90,298 radiographic images were acquired and included in the analysis. The average reject rate was 9%, and the most frequent reasons for image rejection were positioning error (49%) and anatomy cut-off (21%). The reject rate varied between radiographers as well as for individual examination types and projections.
conclusionsThe variation in radiographer reject rates and the high reject rate for some projections indicate that reject analysis is still necessary as a quality assurance tool for DR. A feedback system between radiologists and radiographers may reduce the high percentage of positioning errors by standardising the technical factors used to assess image quality. Future reject analysis should be conducted regularly incorporating an exposure indicator analysis as well as retrospective assessment of individual rejected images.
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