{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T08:02:35Z","timestamp":1768032155283,"version":"3.49.0"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute of the National Institute of Health, USA","doi-asserted-by":"publisher","award":["R01CA196687"],"award-info":[{"award-number":["R01CA196687"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"YES Award through the Department of Radiology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA","doi-asserted-by":"publisher","award":["R25CA221718"],"award-info":[{"award-number":["R25CA221718"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3049781","type":"journal-article","created":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T22:37:00Z","timestamp":1610231820000},"page":"17208-17221","source":"Crossref","is-referenced-by-count":24,"title":["Synthetic CT Generation of the Pelvis in Patients With Cervical Cancer: A Single Input Approach Using Generative Adversarial Network"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0244-5583","authenticated-orcid":false,"given":"Atallah","family":"Baydoun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6101-3257","authenticated-orcid":false,"given":"Ke","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9644-202X","authenticated-orcid":false,"given":"Jin Uk","family":"Heo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8497-3931","authenticated-orcid":false,"given":"Huan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feifei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Latoya A.","family":"Bethell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elisha T.","family":"Fredman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodney J.","family":"Ellis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tarun K.","family":"Podder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Melanie S.","family":"Traughber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7801-1733","authenticated-orcid":false,"given":"Raj M.","family":"Paspulati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5596-3694","authenticated-orcid":false,"given":"Pengjiang","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bryan J.","family":"Traughber","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9986-4674","authenticated-orcid":false,"given":"Raymond F.","family":"Muzic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"An introduction to convolutional neural networks","author":"o\u2019shea","year":"2015","journal-title":"arXiv 1511 08458"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2018.11.013"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aada6d"},{"key":"ref32","author":"pinkney","year":"2020","journal-title":"pix2pix"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref30","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref37","first-page":"39","article-title":"Image synthesis with a convolutional capsule generative adversarial network","author":"bass","year":"2019","journal-title":"Proc Med Imag Deep Learn"},{"key":"ref36","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Med Image Comput Comput -Assist Intervent"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab857b"},{"key":"ref34","article-title":"MR to CT synthesis with multicenter data in the pelvic area using a conditional generative adversarial network","volume":"65","author":"boni","year":"2020","journal-title":"Phys Med Biol"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref61","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_27"},{"key":"ref28","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/s11427-016-0395-3"},{"key":"ref27","author":"smith","year":"2010","journal-title":"The Physics of Clinical MR Taught Through Images"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/72.279181"},{"key":"ref66","article-title":"How deep is deep enough?—Quantifying class separability in the hidden layers of deep neural networkss","author":"schilling","year":"2018","journal-title":"arXiv 1811 01753"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-33843-5_23"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-45934-8"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015150709"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab41af"},{"key":"ref22","author":"bushberg","year":"2011","journal-title":"The Essential Physics of Medical Imaging"},{"key":"ref21","article-title":"FDG-PET\/MR for cervical cancer staging and radiation therapy planning: A novel, deep learning-based approach","author":"baydoun","year":"2020"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14180"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1148\/radiology.153.1.6089263"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-68879-2_10"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.ibmed.2020.100010"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/56\/10\/013"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-31292-2_3"},{"key":"ref59","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.109.064824"},{"key":"ref56","first-page":"636","article-title":"COMKAT: Compartment model kinetic analysis tool","volume":"42","author":"muzic","year":"2001","journal-title":"J Nucl Med"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2018.04.170"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1118\/1.4924598"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12564"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1120\/jacmp.v17i3.6025"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12090"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-014-0252-2"},{"key":"ref40","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.08.043"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10723-020-09513-3"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1118\/1.4873315"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-016-0747-y"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/2\/825"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/59\/23\/7501"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/62\/8\/2935"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13574"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-010-1998-x"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1148\/rg.344140001"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ctro.2019.03.006"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/21\/R297"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12406"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-016-0591-0"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1118\/1.2402331"},{"key":"ref46","article-title":"Fractional max-pooling","author":"graham","year":"2014","journal-title":"arXiv 1412 6071"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1038\/14819"},{"key":"ref48","article-title":"Enhancement of SSD by concatenating feature maps for object detection","author":"jeong","year":"2017","journal-title":"arXiv 1705 09587"},{"key":"ref47","article-title":"A guide to convolution arithmetic for deep learning","author":"dumoulin","year":"2016","journal-title":"ArXiv 1603 07285"},{"key":"ref42","article-title":"Enabling sparse winograd convolution by native pruning","author":"li","year":"2017","journal-title":"arXiv 1702 08597"},{"key":"ref41","first-page":"217","article-title":"Permitted and forbidden sets in symmetric threshold-linear networks","author":"hahnloser","year":"2001","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/261\/1\/012012"},{"key":"ref43","first-page":"2483","article-title":"How does batch normalization help optimization?","author":"santurkar","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09316666.pdf?arnumber=9316666","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:56:02Z","timestamp":1639770962000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9316666\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":66,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3049781","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}