{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T14:07:58Z","timestamp":1791986878889,"version":"4.4.0"},"reference-count":55,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["UL1TR002345"],"award-info":[{"award-number":["UL1TR002345"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007930","name":"Institute of Clinical and Translational Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007930","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2022,11]]},"abstract":"\n Purpose<\/jats:title>\n CT is routinely used to detect cranial abnormalities in pediatric patients with head trauma or craniosynostosis. This study aimed to develop a deep learning method to synthesize pseudo\u2010CT (pCT) images for MR high\u2010resolution pediatric cranial bone imaging to eliminating ionizing radiation from CT.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n 3D golden\u2010angle stack\u2010of\u2010stars MRI were obtained from 44 pediatric participants. Two patch\u2010based residual UNets were trained using paired MR and CT patches randomly selected from the whole head (NetWH) or in the vicinity of bone, fractures\/sutures, or air (NetBA) to synthesize pCT. A third residual UNet was trained to generate a binary brain mask using only MRI. The pCT images from NetWH (pCT\n NetWH<\/jats:sub>\n ) in the brain area and NetBA (pCT\n NetBA<\/jats:sub>\n ) in the nonbrain area were combined to generate pCT\n Com<\/jats:sub>\n . A manual processing method using inverted MR images was also employed for comparison.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n pCT\n Com<\/jats:sub>\n (68.01\u2009\u00b1\u200914.83 HU) had significantly smaller mean absolute errors (MAEs) than pCT\n NetWH<\/jats:sub>\n (82.58\u2009\u00b1\u200916.98 HU,\n P<\/jats:italic>\n \u2009<\u20090.0001) and pCT\n NetBA<\/jats:sub>\n (91.32\u2009\u00b1\u200917.2 HU,\n P<\/jats:italic>\n <\u20090.0001) in the whole head. Within cranial bone, the MAE of pCT\n Com<\/jats:sub>\n (227.92\u2009\u00b1\u200946.88 HU) was significantly lower than pCT\n NetWH<\/jats:sub>\n (287.85\u2009\u00b1\u200959.46 HU,\n P<\/jats:italic>\n \u2009<\u20090.0001) but similar to pCT\n NetBA<\/jats:sub>\n (230.20\u2009\u00b1\u200946.17 HU). Dice similarity coefficient of the segmented bone was significantly higher in pCT\n Com<\/jats:sub>\n (0.90\u2009\u00b1\u20090.02) than in pCT\n NetWH<\/jats:sub>\n (0.86\u2009\u00b1\u20090.04,\n P<\/jats:italic>\n \u2009<\u20090.0001), pCT\n NetBA<\/jats:sub>\n (0.88\u2009\u00b1\u20090.03,\n P<\/jats:italic>\n \u2009<\u20090.0001), and inverted MR (0.71\u2009\u00b1\u20090.09,\n P<\/jats:italic>\n \u2009<\u20090.0001). Dice similarity coefficient from pCT\n Com<\/jats:sub>\n demonstrated significantly reduced age dependence than inverted MRI. Furthermore, pCT\n Com<\/jats:sub>\n provided excellent suture and fracture visibility comparable to CT.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n MR high\u2010resolution pediatric cranial bone imaging may facilitate the clinical translation of a radiation\u2010free MR cranial bone imaging method for pediatric patients.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/mrm.29356","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T08:11:20Z","timestamp":1655453480000},"page":"2285-2297","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Deep\u2010learning synthesized pseudo\u2010\n CT<\/scp>\n for\n MR<\/scp>\n high\u2010resolution pediatric cranial bone imaging (\n MR\u2010HiPCB<\/scp>\n )"],"prefix":"10.1002","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4996-2601","authenticated-orcid":false,"given":"Parna","family":"Eshraghi 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