{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T12:04:03Z","timestamp":1759147443241},"reference-count":45,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T00:00:00Z","timestamp":1681516800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82172865","12105160"],"award-info":[{"award-number":["82172865","12105160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2019LZL012","ZR2021QA099"],"award-info":[{"award-number":["ZR2019LZL012","ZR2021QA099"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Traditional methods of radiotherapy positioning have shortcomings such as fragile skin\u2010markers, additional doses, and lack of information integration. Emerging technologies may provide alternatives for the relevant clinical practice.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To propose a noninvasive radiotherapy positioning system integrating augmented reality (AR) and optical surface, and to evaluate its feasibility in clinical workflow.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>AR and structured light\u2010based surface were integrated to implement the coarse\u2010to\u2010precise positioning through two coherent steps, the AR\u2010based coarse guidance and the optical surface\u2010based precise verification. To implement quality assurance, recognition of face and pattern was used for patient authentication, case association, and accessory validation in AR scenes. The holographic images reconstructed from simulation computed tomography (CT) images, guided the initial posture correction by virtual\u2010real alignment. The point clouds of body surface were fused, with the calibration and pose estimation of structured light cameras, and segmented according to the preset regions of interest (ROIs). The global\u2010to\u2010local registration for cross\u2010source point clouds was achieved to calculate couch shifts in six degrees\u2010of\u2010freedom (DoF), which were ultimately transmitted to AR scenes. The evaluation based on phantom and human\u2010body (4 volunteers) included, (i) quality assurance workflow, (ii) errors of both steps and correlation analysis, (iii) receiver operating characteristic (ROC), (iv) distance characteristics of accuracy, and (v) clinical positioning efficiency.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The maximum errors in phantom evaluation were 3.4\u00a0\u00b1\u00a02.5\u00a0mm in <jats:italic>Vrt<\/jats:italic> and 1.4\u00a0\u00b1\u00a01.0\u00b0 in <jats:italic>Pitch<\/jats:italic> for the coarse guidance step, while 1.6\u00a0\u00b1\u00a00.9\u00a0mm in <jats:italic>Vrt<\/jats:italic> and 0.6\u00a0\u00b1\u00a00.4\u00b0 in <jats:italic>Pitch<\/jats:italic> for the precise verification step. The Pearson correlation coefficients between precise verification and cone beam CT (CBCT) results were distributed in the interval [0.81, 0.85]. In ROC analysis, the areas under the curve (AUC) were 0.87 and 0.89 for translation and rotation, respectively. In human body\u2010based evaluation, the errors of thorax and abdomen (T&amp;A) were significantly greater than those of head and neck (H&amp;N) in <jats:italic>Vrt<\/jats:italic> (2.6\u00a0\u00b1\u00a01.1\u00a0vs. 1.7\u00a0\u00b1\u00a00.8, <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.01), <jats:italic>Lng<\/jats:italic> (2.3\u00a0\u00b1\u00a01.1\u00a0vs. 1.4\u00a0\u00b1\u00a00.9, <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.01), and <jats:italic>Rtn<\/jats:italic> (0.8\u00a0\u00b1\u00a00.4\u00a0vs. 0.6\u00a0\u00b1\u00a00.3, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.01) while relatively similar in <jats:italic>Lat<\/jats:italic> (1.8\u00a0\u00b1\u00a00.9\u00a0vs. 1.7\u00a0\u00b1\u00a00.8, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.07). The translation displacement range, after coarse guidance step, required for high accuracy of the optical surface component of the integrated system was 0\u201342\u00a0mm, and the average positioning duration of the integrated system was significantly less than that of conventional workflow (355.7\u00a0\u00b1\u00a021.7 vs. 387.7\u00a0\u00b1\u00a026.6 s, <jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.01).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The combination of AR and optical surface has utility and feasibility for patient positioning, in terms of both safety and accuracy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16417","type":"journal-article","created":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T12:34:40Z","timestamp":1681562080000},"page":"4505-4520","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Innovative integration of augmented reality and optical surface imaging: A coarse\u2010to\u2010precise system for radiotherapy positioning"],"prefix":"10.1002","volume":"50","author":[{"given":"Gongsen","family":"Zhang","sequence":"first","affiliation":[{"name":"Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Ji'nan Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zejun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Ji'nan Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Ji'nan Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyuan","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Ji'nan Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"He","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Ji'nan Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinchao","family":"Liu","sequence":"additional","affiliation":[{"name":"Cheeloo College of Medicine Shandong University  Ji'nan Shandong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yankui","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Nuclear Science and Technology University of Science and Technology of China  Hefei China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences  Ji'nan Shandong 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