{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T22:48:43Z","timestamp":1761864523535,"version":"build-2065373602"},"reference-count":28,"publisher":"IOP Publishing","issue":"14","license":[{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,7,8]],"date-time":"2025-07-08T00:00:00Z","timestamp":1751932800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100013365","name":"West China Hospital, Sichuan University","doi-asserted-by":"crossref","award":["ZYGD23024"],"award-info":[{"award-number":["ZYGD23024"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective.<\/jats:italic> Chest radiography requires effective quality control (QC) to reduce high retake rates. However, existing QC measures are all retrospective and implemented after exposure, often necessitating retakes when image quality fails to meet standards and thereby increasing radiation exposure to patients. To address this issue, we proposed a 3D human body (3D-HB) reconstruction algorithm to realize prospective QC. Our objective was to investigate the feasibility of using the reconstructed 3D-HB for prospective QC in chest radiography and evaluate its impact on retake rates. <jats:italic>Approach.<\/jats:italic> This prospective study included patients indicated for posteroanterior (PA) and lateral (LA) chest radiography in May 2024. A 3D-HB reconstruction algorithm integrating the Skinned Multi-Person Linear eXpressive model and the Hybrid Analytical-Neural Inverse Kinematics eXpressive algorithm was proposed to convert patients\u2019 2D images into 3D-HBs. QC metrics regarding patient positioning and collimation were assessed using chest radiographs (reference standard) and 3D-HBs, with results compared using intraclass correlation coefficients (ICCs), linear regression, and receiver operating characteristic curves. For retake rate evaluation, a real-time 3D-HB visualization interface was developed and chest radiography was conducted in two four-week phases: the first without prospective QC and the second with prospective QC. Retake rates between the two phases were compared using chi-square tests. <jats:italic>Main results.<\/jats:italic> 324 participants were included (mean age, 42 years \u00b1 19 [SD]; 145 men; 324 PA and 294 LA examinations). The ICCs for the clavicle and midaxillary line angles were 0.80 and 0.78, respectively. Linear regression showed good relation for clavicle angles (<jats:italic>R<\/jats:italic>\n                  <jats:sup>2<\/jats:sup>: 0.655) and midaxillary line angles (<jats:italic>R<\/jats:italic>\n                  <jats:sup>2<\/jats:sup>: 0.616). In PA chest radiography, the AUCs of 3D-HBs were 0.89, 0.87, 0.91 and 0.92 for assessing scapula rotation, lateral tilt, centered positioning and central x-ray alignment respectively, with 97% accuracy in collimation assessment. In LA chest radiography, the AUCs of 3D-HBs were 0.87, 0.84, 0.87 and 0.88 for assessing arms raised, chest rotation, centered positioning and central x-ray alignment respectively, with 94% accuracy in collimation assessment. In retake rate evaluation, 3995 PA and 3295 LA chest radiographs were recorded. The implementation of prospective QC based on the 3D-HB reduced retake rates from 8.6% to 3.5% (PA) and 19.6% to 4.9% (LA) (<jats:italic>p<\/jats:italic> &lt; .001). <jats:italic>Significance.<\/jats:italic> The reconstructed 3D-HB is a feasible tool for prospective QC in chest radiography, providing real-time feedback on patient positioning and collimation before exposure. Prospective QC based on the reconstructed 3D-HB has the potential to reshape the future of radiography QC by significantly reducing retake rates and improving clinical standardization.<\/jats:p>","DOI":"10.1088\/1361-6560\/ade94c","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T22:56:08Z","timestamp":1751064968000},"page":"145004","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Prospective quality control in chest radiography based on the reconstructed 3D human body"],"prefix":"10.1088","volume":"70","author":[{"given":"Yuqi","family":"Tan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyu","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhou","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejun","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanyu","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunchao","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7525-330X","authenticated-orcid":true,"given":"Zhenlin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,7,8]]},"reference":[{"key":"pmbade94cbib1","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1007\/s00330-018-5745-z","article-title":"Accuracy of automated patient positioning in CT using a 3D camera for body contour detection","volume":"29","author":"Booij","year":"2019","journal-title":"Eur. 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