{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:03:34Z","timestamp":1790229814834,"version":"4.1.0"},"reference-count":26,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The deviation index (DI) is a vendor\u2010neutral indicator of detector exposure relative to the configured target exposure index (TEI). Because TEI is a protocol value rather than a calibrated physical quantity, poorly aligned TEI settings can create systematic DI offsets, obscure protocol problems, and prevent meaningful statistical process control.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This retrospective multi\u2010vendor study evaluated whether routine DICOM metadata can identify biased DI distributions, separate fixed TEI\u2010setting offsets from protocol\u2010adherence variation, estimate candidate TEI updates, and support TG\u2010232\u2010aligned standard deviation\u2010based monitoring after DI re\u2010centering.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analyzed 38\u00a0916 digital radiography examinations performed from January 1, 2024 through August 27, 2024 using three vendor platforms at a tertiary hospital. DICOM metadata were benchmarked against published technical standards using a composite adherence index (CAI) for SID, kVp, mAs, grid use, and AEC use. A secondary gradient\u2010boosting\/SHAP analysis was retained as a non\u2010causal variance discovery to rank the metadata patterns used to predict DI. Candidate TEI updates were estimated within the vendor\u2010region\u2010projection groups by algebraically re\u2010centering the observed mean DI ( to 0.0. Robustness was assessed using sensitivity analyses restricted by automatic exposure control, beam energy, and patient habitus proxy measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The baseline \u00a0=\u00a0\u22123.1 (SD 3.7), and only 11.3% of examinations were within |DI| \u2264 1. Forty of 45 vendor\u2013region\u2013projection groups (88.9%) had an absolute group  greater than 1.0. Separately, the mean CAI was 0.176. The secondary SHAP analysis ranked the configured TEI, manufacturer, SID, exposure, and kVp as high\u2010contribution predictors of DI; these findings were interpreted as predictive attribution, not causal evidence. DI re\u2010centering reduced the overall variance by 42.9% (13.5 to 7.7), increased the proportion of examinations within |DI| \u2264 1 to 34.3%, and reduced severe deviations (|DI|\u00a0&gt;\u00a03) from 60.3% to 23.1%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In this retrospective, multi\u2010vendor study, the TEI setting review was operationalized as a structured, data\u2010driven responsibility of a clinical site. By verifying protocol adherence, equipment performance, image quality, and dose indicators before centering the , departments can support the conversion of the DI from a biased compliance signal into a practical process\u2010control metric for exposure governance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70820","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:28:48Z","timestamp":1790227728000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging TG\u2010116 and TG\u2010232: A retrospective multi\u2010vendor evaluation of deviation index re\u2010centering and process control in digital radiography"],"prefix":"10.1002","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5806-3130","authenticated-orcid":false,"given":"Ezzat","family":"AbuAzzah","sequence":"first","affiliation":[{"name":"Department of Diagnostic Radiology College of 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