{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T18:13:09Z","timestamp":1773425589073,"version":"3.50.1"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001411","name":"Indian Council of Medical Research","doi-asserted-by":"publisher","award":["FIW-2024\u201301-00000151"],"award-info":[{"award-number":["FIW-2024\u201301-00000151"]}],"id":[{"id":"10.13039\/501100001411","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-026-01900-8","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T17:43:28Z","timestamp":1773423808000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploration of Deep Learning Methods for Synthetic T2-Weighted Pelvic MRI Generation from CT Scans: A Technical Feasibility Study"],"prefix":"10.1007","author":[{"given":"Peeyush Kumar","family":"Singh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inam Ul Haq","family":"Gulzar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pankaj","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niharika","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Priya","family":"Mudgil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raghuraman","family":"Soundararajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhinandan","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kartik","family":"Bose","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ajay","family":"Gulati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mandeep","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Nigam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sneha","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"1900_CR1","doi-asserted-by":"crossref","unstructured":"Horvat N, Carlos Tavares Rocha C, Clemente Oliveira B, Petkovska I, Gollub MJ. MRI of rectal cancer: tumor staging, imaging techniques, and management. Radiographics. 2019 Mar;39(2):367\u201387.","DOI":"10.1148\/rg.2019180114"},{"issue":"92","key":"1900_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103046","volume":"1","author":"S Dayarathna","year":"2024","unstructured":"Dayarathna S, Islam KT, Uribe S, Yang G, Hayat M, Chen Z. Deep learning based synthesis of MRI, CT and PET: Review and analysis. Medical image analysis. 2024;92:103046.","journal-title":"Medical image analysis."},{"issue":"1","key":"1900_CR3","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1002\/acm2.13121","volume":"22","author":"T Wang","year":"2021","unstructured":"Wang T, Lei Y, Fu Y, Wynne JF, Curran WJ, Liu T, Yang X. A review on medical imaging synthesis using deep learning and its clinical applications. Journal of applied clinical medical physics. 2021;22(1):11-36.","journal-title":"Journal of applied clinical medical physics."},{"issue":"8","key":"1900_CR4","doi-asserted-by":"publisher","first-page":"3565","DOI":"10.1002\/mp.13617","volume":"46","author":"Y Lei","year":"2019","unstructured":"Lei Y, Harms J, Wang T, Liu Y, Shu HK, Jani AB, Curran WJ, Mao H, Liu T, Yang X. MRI\u2010only based synthetic CT generation using dense cycle consistent generative adversarial networks. Medical physics. 2019 ;46(8):3565-81.","journal-title":"Medical physics."},{"issue":"9","key":"1900_CR5","doi-asserted-by":"publisher","first-page":"3788","DOI":"10.1002\/mp.13672","volume":"46","author":"J Fu","year":"2019","unstructured":"Fu J, Yang Y, Singhrao K, Ruan D, Chu FI, Low DA, Lewis JH. Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging. Medical physics. 2019;46(9):3788-98.","journal-title":"Medical physics."},{"issue":"112","key":"1900_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108627","volume":"1","author":"Y Ni","year":"2026","unstructured":"Ni Y, Ma J, Chen J. Medical volume CT-to-MRI translation with multi-dimensional diffusion architecture. Biomedical Signal Processing and Control. 2026;112:108627.","journal-title":"Biomedical Signal Processing and Control."},{"issue":"3","key":"1900_CR7","doi-asserted-by":"publisher","first-page":"791","DOI":"10.3390\/biomedinformatics3030050","volume":"3","author":"J McNaughton","year":"2023","unstructured":"McNaughton J, Holdsworth S, Chong B, Fernandez J, Shim V, Wang A. Synthetic MRI generation from CT scans for stroke patients. BioMedInformatics. 2023 Sep 11;3(3):791-816.","journal-title":"BioMedInformatics."},{"issue":"6","key":"1900_CR8","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.21037\/qims-19-885","volume":"10","author":"W Li","year":"2020","unstructured":"Li W, Li Y, Qin W, Liang X, Xu J, Xiong J, Xie Y. Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy. Quantitative imaging in medicine and surgery. 2020;10(6):1223.","journal-title":"Quantitative imaging in medicine and surgery."},{"issue":"11","key":"1900_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.665807","volume":"30","author":"R Kalantar","year":"2021","unstructured":"Kalantar R, Messiou C, Winfield JM, Renn A, Latifoltojar A, Downey K, Sohaib A, Lalondrelle S, Koh DM, Blackledge MD. CT-based pelvic T1-weighted MR image synthesis using UNet, UNet++ and cycle-consistent generative adversarial network (Cycle-GAN). Frontiers in oncology. 2021;11:665807.","journal-title":"Frontiers in oncology."},{"issue":"5","key":"1900_CR10","doi-asserted-by":"publisher","first-page":"2861","DOI":"10.1002\/mp.17674","volume":"52","author":"YF Xia","year":"2025","unstructured":"Xia YF, Zeng M, Sun SW, Liu QP, Zhang JL, Zhi R, Lu FY, Chen W, Zhang YD. Region\u2010guided focal adversarial learning for CT\u2010to\u2010MRI translation: A proof\u2010of\u2010concept and validation study in hepatocellular carcinoma. Medical Physics. 2025;52(5):2861-73.","journal-title":"Medical Physics."},{"key":"1900_CR11","doi-asserted-by":"crossref","unstructured":"Chen Z, Gao Q, Zhang Y, Shan H. ASCON: Anatomy-aware supervised contrastive learning framework for low-dose CT denoising. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention 2023 Oct 1 (pp. 355\u2013365). Cham: Springer Nature Switzerland.","DOI":"10.1007\/978-3-031-43999-5_34"},{"issue":"10","key":"1900_CR12","doi-asserted-by":"publisher","first-page":"2598","DOI":"10.1109\/TMI.2022.3167808","volume":"41","author":"O Dalmaz","year":"2022","unstructured":"Dalmaz O, Yurt M, \u00c7ukur T. ResViT: Residual vision transformers for multimodal medical image synthesis. IEEE Transactions on Medical Imaging. 2022;41(10):2598-614.","journal-title":"IEEE Transactions on Medical Imaging."},{"key":"1900_CR13","unstructured":"Ghasemabadi A, Janjua MK, Salameh M, Zhou C, Sun F, Niu D. Cascadedgaze: Efficiency in global context extraction for image restoration. arXiv preprint arXiv:2401.15235. 2024 Jan 26."},{"issue":"7","key":"1900_CR14","doi-asserted-by":"publisher","first-page":"4664","DOI":"10.1002\/mp.16529","volume":"50","author":"A Thummerer","year":"2023","unstructured":"Thummerer A, Van der Bijl E, Galapon Jr A, Verhoeff JJ, Langendijk JA, Both S, van den Berg CN, Maspero M. SynthRAD2023 Grand Challenge dataset: Generating synthetic CT for radiotherapy. Medical physics. 2023 ;50(7):4664-74.","journal-title":"Medical physics."},{"key":"1900_CR15","unstructured":"https:\/\/clinicaltrials.gov\/ct2\/show\/NCT05716633"},{"issue":"3","key":"1900_CR16","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1002\/mp.12748","volume":"45","author":"T Nyholm","year":"2018","unstructured":"Nyholm T, Svensson S, Andersson S, Jonsson J, Sohlin M, Gustafsson C, Kjell\u00e9n E, S\u00f6derstr\u00f6m K, Albertsson P, Blomqvist L, Zackrisson B. MR and CT data with multiobserver delineations of organs in the pelvic area\u2014Part of the Gold Atlas project. Medical physics. 2018;45(3):1295-300.","journal-title":"Medical physics."},{"key":"1900_CR17","unstructured":"D'Antonoli TA, Berger LK, Indrakanti AK, Vishwanathan N, Wei\u00df J, Jung M, Berkarda Z, Rau A, Reisert M, K\u00fcstner T, Walter A. TotalSegmentator MRI: Robust sequence-independent segmentation of multiple anatomic structures in MRI. arXiv preprint arXiv:2405.19492. 2024 May 29."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-01900-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-01900-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-01900-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T17:43:31Z","timestamp":1773423811000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-01900-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,13]]},"references-count":17,"alternative-id":["1900"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-01900-8","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,13]]},"assertion":[{"value":"4 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the Institutional Ethics Committee (PGI\/IEC\/2025\/EICOO0572).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Not required as no patient identifying information is shown.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}