{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T03:15:45Z","timestamp":1791083745997,"version":"4.1.0"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T00:00:00Z","timestamp":1764806400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Medical Radiation Sci"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Image reject analysis is a critical quality assurance (QA) tool in diagnostic imaging, helping to minimise unnecessary radiation exposure and improve imaging efficiency. This study evaluates image rejection patterns in a computed radiography (CR) system at a major tertiary teaching hospital in Ghana, identifying key sources of errors and their implications for radiology practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective review of radiographic images acquired between April and June 2023 was conducted. Images, including those flagged as rejects were retrieved from the\n                      <jats:styled-content style=\"fixed-case\">CR<\/jats:styled-content>\n                      system and analysed for rejection rates, trends by anatomical region, and key error sources.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Of the 5889 images reviewed, 974 were rejected, resulting in an overall rejection rate of 16.5%. Rejection rates varied considerably across anatomical regions. High rejection rates were observed in skull\/sinus (34.9%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200990\/258), pelvic (29.9%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200988\/294) and abdomen (26.9%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200984\/312) examinations. Low rejects were recorded for ankle (1.8%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092\/110), humerus (2.4%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20092\/82), forearm (6.7%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20096\/90), elbow (9.7%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20096\/62), and lower leg (7.5%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200916\/214). Across all examinations, the three leading causes of image rejection were anatomical cut\u2010off (40.5%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009394), positioning errors (27.5%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009268), and beam centering errors (18.5%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u2009180). Less frequent causes included exposure\u2010related issues (6.6%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200964), patient movement (2.9%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200928), and artefacts or ghosting (4.1%,\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200940).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study reinforces the role of image reject analysis as a valuable\n                      <jats:styled-content style=\"fixed-case\">QA<\/jats:styled-content>\n                      measure in\n                      <jats:styled-content style=\"fixed-case\">CR<\/jats:styled-content>\n                      systems. The high rejection rates observed highlight the need for targeted interventions in positioning, workflow optimization, and radiographer training, particularly in resource\u2010constrained settings to enhance diagnostic quality and patient safety.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmrs.70039","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T14:40:28Z","timestamp":1764859228000},"page":"87-95","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Image Reject Patterns in Computed Radiography: Insights From a Ghanaian Radiology Department"],"prefix":"10.1002","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5727-0210","authenticated-orcid":false,"given":"Bismark","family":"Ofori\u2010Manteaw","sequence":"first","affiliation":[{"name":"Medical Radiation Science Discipline, School of Dentistry and Medical Science, Faculty of 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