{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:04:47Z","timestamp":1784217887985,"version":"3.55.0"},"reference-count":23,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2023A1515012499"],"award-info":[{"award-number":["2023A1515012499"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2025A1515010318"],"award-info":[{"award-number":["2025A1515010318"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The lens of the eye is highly radiosensitive, yet personalized shielding during head CT remains challenging due to the lack of a rapid, pre\u2010scan localization method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and validate a deep learning solution that enables automated, patient\u2010specific eye protection by generating a precise 3D eyeball model directly from a single\u2010view topogram.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our two\u2010stage approach combines an advanced data simulation pipeline\u2014which generates realistic training topograms from digitally reconstructed radiographs (DRRs) using a table\u2010movement\u2010aware model and CycleGAN\u2010based stylization\u2014with a dedicated generative network (EyeGen\u2010Net). The model was trained on 400 synthetic and validated on 100 real clinical samples.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>EyeGen\u2010Net achieved a Dice Similarity Coefficient of 0.79\u00a0\u00b1\u00a00.08, a Hausdorff Distance of 5.40\u00a0\u00b1\u00a01.57\u00a0mm, and an Average Surface Distance of 1.84\u00a0\u00b1\u00a00.65\u00a0mm against expert segmentations. Crucially, phantom validation demonstrated that the derived 3D model facilitates organ\u2010based tube current modulation (OBTCM), yielding an approximate 30% reduction in lens dose across different scanning modes without compromising diagnostic image quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This work provides a practical, automated pathway for implementing personalized radioprotection in routine head CT, aligning with the ALARA (As Low As Reasonably Achievable) principle.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70665","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T04:46:53Z","timestamp":1782276413000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Personalized eye protection for head CT organ\u2010based tube current modulation: A deep learning approach to derive 3D eyeball models from a single\u2010view topogram"],"prefix":"10.1002","volume":"27","author":[{"given":"Xiaolin","family":"Meng","sequence":"first","affiliation":[{"name":"School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University  Shanghai China"},{"name":"Healthcare Advanced Algorithm Department of HSW BU Shanghai United Imaging Healthcare Co., Ltd  Shanghai P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Healthcare Advanced Algorithm Department of HSW BU Shanghai United Imaging Healthcare Co., Ltd  Shanghai P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenghao","family":"Chen","sequence":"additional","affiliation":[{"name":"Correction Algorithm Department of CT BU Shanghai United Imaging Healthcare Co., Ltd  Shanghai P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manhua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University  Shanghai China"},{"name":"MoE Key Laboratory of Artificial Intelligence, AI Institute Shanghai Jiao Tong University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology Zhujiang Hospital of Southern Medical University  Guangzhou China"},{"name":"Department of Radiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"key":"e_1_2_11_2_1","article-title":"Prospective optimization of CT under tube current modulation: i. organ dose","volume":"9033","author":"Tian X","year":"2014","journal-title":"Spie Medical Imaging"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.12.9652"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrs.2012.11.016"},{"issue":"10","key":"e_1_2_11_5_1","first-page":"650","article-title":"[Ionizing radiation and the risk for cataract and lens opacities]","volume":"157","author":"Peled A","year":"2018","journal-title":"Harefuah"},{"key":"e_1_2_11_6_1","article-title":"Radiation exposure by medical X\u2010ray applications","volume":"20","author":"Buchberger B","year":"2022","journal-title":"Ger Med Sci"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-022-10310-2"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20210471"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02620-0"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00247-024-05949-x"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.icrp.2012.02.001"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0b013e3182311504"},{"key":"e_1_2_11_13_1","unstructured":"RekdalAL Radiation dose and image quality in CT: comparison and evaluation of two different organ\u2010based tube current modulation techniques and their impact on organ dose and image noise [master's thesis].Norwegian University of Science and Technology;2019."},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-017-0755-5"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.10.6061"},{"key":"e_1_2_11_16_1","doi-asserted-by":"crossref","unstructured":"FuW SturgeonGM AgasthyaG et\u00a0al.Estimation of breast dose reduction potential for organ\u2010based tube current modulated CT with wide dose reduction arc. 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