{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T07:37:27Z","timestamp":1792741047488,"version":"4.5.0"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2025-16069396"],"award-info":[{"award-number":["RS-2025-16069396"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.ejrad.2026.113227","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T15:01:53Z","timestamp":1788879713000},"page":"113227","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Performance of a deep learning algorithm on deep learning reconstruction\u2013accelerated prostate biparametric MRI: A preliminary study"],"prefix":"10.1016","volume":"205","author":[{"given":"Young Joon","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5962-4772","authenticated-orcid":false,"given":"Moon Hyung","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1233-8215","authenticated-orcid":false,"given":"Robert","family":"Grimm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heinrich","family":"von Busch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-7233","authenticated-orcid":false,"given":"Marcel Dominik","family":"Nickel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2026.113227_b0005","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1016\/S0140-6736(16)32401-1","article-title":"Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study","volume":"389","author":"Ahmed","year":"2017","journal-title":"Lancet"},{"key":"10.1016\/j.ejrad.2026.113227_b0010","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1056\/NEJMoa1801993","article-title":"MRI-targeted or standard biopsy for prostate-cancer diagnosis","volume":"378","author":"Kasivisvanathan","year":"2018","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.ejrad.2026.113227_b0015","doi-asserted-by":"crossref","first-page":"625","DOI":"10.3348\/kjr.2022.0059","article-title":"Paradigm shift in prostate cancer diagnosis: pre-biopsy prostate magnetic resonance imaging and targeted biopsy","volume":"23","author":"Park","year":"2022","journal-title":"Korean J. Radiol."},{"issue":"76","key":"10.1016\/j.ejrad.2026.113227_b0020","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.eururo.2019.02.033","article-title":"Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2","volume":"2019","author":"Turkbey","year":"2019","journal-title":"Eur. Urol."},{"key":"10.1016\/j.ejrad.2026.113227_b0025","doi-asserted-by":"crossref","first-page":"1439","DOI":"10.1148\/rg.2015140289","article-title":"Body MR Imaging: Artifacts, k-Space, and Solutions","volume":"35","author":"Huang","year":"2015","journal-title":"Radiographics"},{"key":"10.1016\/j.ejrad.2026.113227_b0030","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1001\/jama.2025.13722","article-title":"Biparametric vs multiparametric MRI for prostate cancer diagnosis: the PRIME diagnostic clinical trial","volume":"334","author":"Ng","year":"2025","journal-title":"JAMA"},{"key":"10.1016\/j.ejrad.2026.113227_b0035","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.eururo.2024.09.035","article-title":"Evaluating biparametric versus multiparametric magnetic resonance imaging for diagnosing clinically significant prostate cancer: an international, paired, noninferiority confirmatory observer study","volume":"87","author":"Twilt","year":"2025","journal-title":"Eur. Urol."},{"key":"10.1016\/j.ejrad.2026.113227_b0040","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1038\/s41391-025-00999-0","article-title":"Head-to-head comparison of diagnostic test accuracy between biparametric and multiparametric MRI: an updated systematic review and bivariate meta-analysis","volume":"28","author":"Garcia-Becerra","year":"2025","journal-title":"Prostate Cancer Prostatic Dis."},{"key":"10.1016\/j.ejrad.2026.113227_b0045","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3348\/jksr.2023.0067","article-title":"Advanced abdominal MRI techniques and problem-solving strategies","volume":"85","author":"Lee","year":"2024","journal-title":"J. Korean Soc. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0050","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.3348\/kjr.2018.0931","article-title":"Rapid imaging: recent advances in abdominal MRI for reducing acquisition time and its clinical applications","volume":"20","author":"Yoon","year":"2019","journal-title":"Korean J. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0055","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.230427","article-title":"Deep learning super-resolution reconstruction for fast and motion-robust T2-weighted prostate MRI","volume":"308","author":"Bischoff","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2026.113227_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2021.109600","article-title":"Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality","volume":"137","author":"Gassenmaier","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0065","doi-asserted-by":"crossref","first-page":"3593","DOI":"10.3390\/cancers13143593","article-title":"Accelerated T2-weighted TSE imaging of the prostate using deep learning image reconstruction: a prospective comparison with standard T2-weighted TSE imaging","volume":"13","author":"Gassenmaier","year":"2021","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejrad.2026.113227_b0070","doi-asserted-by":"crossref","first-page":"5741","DOI":"10.3390\/cancers14235741","article-title":"Prospectively accelerated T2-weighted imaging of the prostate by combining compressed SENSE and deep learning in patients with histologically proven prostate cancer","volume":"14","author":"Harder","year":"2022","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.ejrad.2026.113227_b0075","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1002\/jmri.28024","article-title":"Deep learning reconstruction enables highly accelerated biparametric MR imaging of the prostate","volume":"56","author":"Johnson","year":"2022","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.ejrad.2026.113227_b0080","doi-asserted-by":"crossref","first-page":"29249","DOI":"10.1038\/s41598-024-79348-5","article-title":"Deep learning-accelerated T2WI of the prostate for transition zone lesion evaluation and extraprostatic extension assessment","volume":"14","author":"Kim","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejrad.2026.113227_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2021.110012","article-title":"Deep learning-accelerated T2-weighted imaging of the prostate: Impact of further acceleration with lower spatial resolution on image quality","volume":"145","author":"Kim","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2023.110953","article-title":"Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: a retrospective comparison with standard diffusion-weighted imaging","volume":"165","author":"Ursprung","year":"2023","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0095","doi-asserted-by":"crossref","first-page":"7364","DOI":"10.1007\/s00330-024-10771-y","article-title":"Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics","volume":"34","author":"van Lohuizen","year":"2024","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0100","doi-asserted-by":"crossref","first-page":"7053","DOI":"10.1007\/s00330-024-10776-7","article-title":"Performance of an ultra-fast deep-learning accelerated MRI screening protocol for prostate cancer compared to a standard multiparametric protocol","volume":"34","author":"Oerther","year":"2024","journal-title":"Eur. Radiol."},{"issue":"396","key":"10.1016\/j.ejrad.2026.113227_b0105","doi-asserted-by":"crossref","first-page":"396.e1","DOI":"10.1016\/j.urolonc.2024.10.002","article-title":"Inter-reader reliability and diagnostic accuracy of PI-RADS scoring between academic and community care networks: how wide is the gap?","volume":"43","author":"Smani","year":"2025","journal-title":"Urol. Oncol."},{"key":"10.1016\/j.ejrad.2026.113227_b0110","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1111\/bju.16452","article-title":"Perspectives on technology: prostate imaging-reporting and data system (PI-RADS) interobserver variability","volume":"134","author":"Taya","year":"2024","journal-title":"BJU Int."},{"key":"10.1016\/j.ejrad.2026.113227_b0115","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.1013941","article-title":"Inter-reader agreement of the prostate imaging reporting and data system version v2.1 for detection of prostate cancer: a systematic review and meta-analysis","volume":"12","author":"Wen","year":"2022","journal-title":"Front. Oncol."},{"key":"10.1016\/j.ejrad.2026.113227_b0120","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.232788","article-title":"MRI-based deep learning algorithm for assisting clinically significant prostate cancer detection: a bicenter prospective study","volume":"314","author":"Lee","year":"2025","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2026.113227_b0125","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1097\/RLI.0000000000000780","article-title":"A novel deep learning based computer-aided diagnosis system improves the accuracy and efficiency of radiologists in reading biparametric magnetic resonance images of the prostate: results of a multireader multicase study","volume":"56","author":"Winkel","year":"2021","journal-title":"Invest. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2021.109894","article-title":"Detection and PI-RADS classification of focal lesions in prostate MRI: Performance comparison between a deep learning-based algorithm (DLA) and radiologists with various levels of experience","volume":"142","author":"Youn","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0135","doi-asserted-by":"crossref","first-page":"42","DOI":"10.21037\/cco-22-104","article-title":"The added value of a deep learning-based computer-aided detection system on prostate cancer detection among readers with varying level of multiparametric MRI expertise","volume":"11","author":"Yilmaz","year":"2022","journal-title":"Chin. Clin. Oncol."},{"key":"10.1016\/j.ejrad.2026.113227_b0140","doi-asserted-by":"crossref","first-page":"335","DOI":"10.3390\/jimaging11100335","article-title":"Artificial intelligence in prostate MRI: current evidence and clinical translation challenges-a narrative review","volume":"11","author":"Bolocan","year":"2025","journal-title":"J. Imaging"},{"key":"10.1016\/j.ejrad.2026.113227_b0145","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.mri.2023.01.010","article-title":"Improved diffusion-weighted imaging of the prostate: Comparison of readout-segmented and zoomed single-shot imaging","volume":"98","author":"Klingebiel","year":"2023","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.ejrad.2026.113227_b0150","doi-asserted-by":"crossref","first-page":"3233","DOI":"10.1007\/s00330-014-3347-y","article-title":"Diffusion-weighted MRI of the prostate: advantages of Zoomed EPI with parallel-transmit-accelerated 2D-selective excitation imaging","volume":"24","author":"Thierfelder","year":"2014","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0155","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.ejrad.2016.02.020","article-title":"Diffusion-weighted imaging in the assessment of prostate cancer: comparison of zoomed imaging and conventional technique","volume":"85","author":"Brendle","year":"2016","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0160","doi-asserted-by":"crossref","first-page":"5659","DOI":"10.1007\/s00261-021-03268-5","article-title":"Better lesion conspicuity translates into improved prostate cancer detection: comparison of non-parallel-transmission-zoomed-DWI with conventional-DWI","volume":"46","author":"Hu","year":"2021","journal-title":"Abdom. Radiol. (NY)"},{"key":"10.1016\/j.ejrad.2026.113227_b0165","doi-asserted-by":"crossref","first-page":"2132","DOI":"10.21037\/qims-24-1263","article-title":"Improved prostate cancer diagnosis: upgraded prostate imaging reporting and data system (PI-RADS) scores by zoomed diffusion-weighted imaging enhance deep-learning-based computer-aided diagnosis accuracy","volume":"15","author":"Long","year":"2025","journal-title":"Quant. Imaging Med. Surg."},{"key":"10.1016\/j.ejrad.2026.113227_b0170","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40644-023-00527-0","article-title":"Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique","volume":"23","author":"Hu","year":"2023","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.ejrad.2026.113227_b0175","doi-asserted-by":"crossref","first-page":"6718","DOI":"10.1016\/j.acra.2025.06.022","article-title":"Deep learning-accelerated prostate MRI: improving speed, accuracy, and sustainability","volume":"32","author":"Reschke","year":"2025","journal-title":"Acad. Radiol."},{"key":"10.1016\/j.ejrad.2026.113227_b0180","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1002\/jmri.28602","article-title":"Comparison of a Deep Learning-Accelerated vs. conventional T2-Weighted Sequence in Biparametric MRI of the Prostate","volume":"58","author":"Tong","year":"2023","journal-title":"J. Magn. Reson. Imaging"},{"key":"10.1016\/j.ejrad.2026.113227_b0185","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20211378","article-title":"Convolutional neural network-based reconstruction for acceleration of prostate T(2) weighted MR imaging: a retro- and prospective study","volume":"95","author":"Jung","year":"2022","journal-title":"Br. J. Radiol."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26005759?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26005759?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T06:38:12Z","timestamp":1792737492000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X26005759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":37,"alternative-id":["S0720048X26005759"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.113227","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Performance of a deep learning algorithm on deep learning reconstruction\u2013accelerated prostate biparametric MRI: A preliminary study","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.113227","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113227"}}