{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:53:05Z","timestamp":1770900785588,"version":"3.50.1"},"reference-count":45,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Accurate estimations of fetal absorbed dose and radiation risks are crucial for radiation protection and important for radiological imaging research owing to the high radiosensitivity of the fetus. Computational anthropomorphic models have been widely used in patient\u2010specific radiation dosimetry calculations. In this work, we aim to build the first digital fetal library for more reliable and accurate radiation dosimetry studies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Acquisition and validation methods<\/jats:title><jats:p>Computed tomography (CT) images of abdominal and pelvic regions of 46 pregnant females were segmented by experienced medical physicists. The segmented tissues\/organs include the body contour, skeleton, uterus, liver, kidney, intestine, stomach, lung, bladder, gall bladder, spleen, and pancreas for maternal body, and placenta, amniotic fluid, fetal body, fetal brain, and fetal skeleton. Nonuniform rational B\u2010spline (NURBS) surfaces of each identified region was constructed manually using 3D modeling software. The Hounsfield unit values of each identified organs were gathered from CT images of pregnant patients and converted to tissue density. Organ volumes were further adjusted according to reference measurements for the developing fetus recommended by the World Health Organization (WHO) and International Commission on Radiological Protection. A series of anatomical parameters, including femur length, humerus length, biparietal diameter, abdominal circumference (FAC), and head circumference, were measured and compared with WHO recommendations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data format and usage notes<\/jats:title><jats:p>The first fetal patient\u2010specific model library was developed with the anatomical characteristics of each model derived from the corresponding patient whose gestational age varies between 8 and 35 weeks. Voxelized models are represented in the form of MCNP matrix input files representing the three\u2010dimensional model of the fetus. The size distributions of each model are also provided in text files. All data are stored on Zenodo and are publicly accessible on the following link: <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/zenodo.org\/record\/6471884\">https:\/\/zenodo.org\/record\/6471884<\/jats:ext-link>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Potential applications<\/jats:title><jats:p>The constructed fetal models and maternal anatomical characteristics are consistent with the corresponding patients. The resulting computational fetus could be used in radiation dosimetry studies to improve the reliability of fetal dosimetry and radiation risks assessment. The advantages of NURBS surfaces in terms of adapting fetal postures and positions enable us to adequately assess their impact on radiation dosimetry calculations.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15905","type":"journal-article","created":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T12:12:56Z","timestamp":1660392776000},"page":"2577-2589","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Construction of a digital fetus library for radiation dosimetry"],"prefix":"10.1002","volume":"50","author":[{"given":"Shuiyin","family":"Qu","sequence":"first","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianwu","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai China"},{"name":"Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital  Geneva Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryellen L.","family":"Giger","sequence":"additional","affiliation":[{"name":"Department of Radiology, Committee on Medical Physics University of Chicago  Chicago Illinois USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianqing","family":"Mao","sequence":"additional","affiliation":[{"name":"Faculty of Science Technology and Medicine (FSTM), Department of Life Sciences and Medicine University of Luxembourg  Esch\u2010sur\u2010Alzette Luxembourg"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Habib","family":"Zaidi","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital  Geneva Switzerland"},{"name":"Geneva Neuroscience Center Geneva University  Geneva Switzerland"},{"name":"Department of Nuclear Medicine and Molecular Imaging University of Groningen University Medical Center Groningen  Groningen The Netherlands"},{"name":"Department of Nuclear Medicine University of Southern Denmark  Odense Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,8,23]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.72.860.10624343"},{"key":"e_1_2_8_3_1","unstructured":"ICRP.Publication 84: pregnancy and medical radiation. 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