{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:03:39Z","timestamp":1791306219237,"version":"4.3.1"},"reference-count":25,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,6,6]],"date-time":"2024-06-06T00:00:00Z","timestamp":1717632000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Medical Radiation Sci"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Reject analysis in digital radiography helps guide the training of staff to reduce patient radiation dose and improve department efficiency. The purpose of this study was to perform a multi\u2010centre, vendor agnostic reject analysis across different room usage types, and to provide benchmarks for comparison.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Retrospective reject and exposure log data were collected via USB from fixed general X\u2010ray systems across multiple Australian sites, for collation and analysis. The overall reject rate, local reject reference level, absolute and relative reject rates for body part categories, reject rates by room usage types and the reject rate for each reason of rejection were calculated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Data were collected from 44 X\u2010ray systems, across 11 hospitals. A total of 2,031,713 acquired images and 172,495 rejected images were included. The median reject rate was 9.1%. The local reject reference level (LRRL), set as the 75<jats:italic>th<\/jats:italic> percentile of all reject rates, was 10.6%. Median reject rates by room type were emergency (7.4%), inpatients\u2009+\u2009outpatients (9.6%), outpatients (9.2%), and hybrid (10.1%). The highest absolute reject rates by body part were chest (2.1%) and knee (1.4%). The highest relative rates by body part were knee (18.1%) and pelvis (17.2%). The most frequent reasons for image rejection were patient positioning (76%) and patient motion (7.5%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The results compare well with previously published data. The range of reject rates highlights the need to analyse typical reject rates in different ways. With analysis feedback to participating sites and the implementation of standardised reject reasons, future analysis should monitor whether reject rates reduce.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmrs.796","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T04:49:35Z","timestamp":1717735775000},"page":"412-420","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multi\u2010centre digital radiography reject analysis for different clinical room use types: The establishment of local reject reference levels for public hospital departments"],"prefix":"10.1002","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4599-0126","authenticated-orcid":false,"given":"Daniel","family":"Serra","sequence":"first","affiliation":[{"name":"Biomedical Technology Services Queensland Health  Coopers Plains Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J","family":"Neep","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging Logan Hospital  Meadowbrook Queensland Australia"},{"name":"School of Clinical Sciences Queensland University of Technology, Gardens Point  Brisbane Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elaine","family":"Ryan","sequence":"additional","affiliation":[{"name":"Biomedical Technology Services Queensland Health  Coopers Plains Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,6,6]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13244-022-01169-9"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-009-9236-2"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2016.07.011"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.343"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4932623"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13938"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.468"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.654"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.5334\/jbsr.3259"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrras.2023.100556"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jrras.2022.01.023"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42058-021-00070-6"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.15537\/smj.2021.42.9.20210254"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.4103\/JQSH.JQSH_24_18"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.5455\/ajdi.20180830110208"},{"key":"e_1_2_10_17_1","article-title":"Reject analysis in digital radiography: a prospective study","volume":"7","author":"Yurt A","year":"2018","journal-title":"Int J Anat Radiol Surg"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1177\/2058460115604339"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2014.12.003"},{"key":"e_1_2_10_20_1","first-page":"879","article-title":"Direct digital radiograph. 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