{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T20:58:04Z","timestamp":1790456284660,"version":"4.1.0"},"reference-count":21,"publisher":"Elsevier BV","issue":"6","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"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jmirs.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Medical Imaging and Radiation Sciences"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.jmir.2026.102604","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T11:50:40Z","timestamp":1790337040000},"page":"102604","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feasibility and image quality evaluation of accelerated T2-weighted and time-of-flight MRI using AI-assisted compressed sensing and deep reconstruction"],"prefix":"10.1016","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3379-0270","authenticated-orcid":false,"given":"Minghui","family":"Tang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9021-347X","authenticated-orcid":false,"given":"Noriyuki","family":"Fujima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Kameda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yohei","family":"Ikebe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Tong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tsubasa","family":"Kaji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0036-8975","authenticated-orcid":false,"given":"Kenji","family":"Hirata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5351-9242","authenticated-orcid":false,"given":"Kohsuke","family":"Kudo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jmir.2026.102604_bib0001","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1002\/mrm.21391","article-title":"Sparse MRI: the application of compressed sensing for rapid MR imaging","volume":"58","author":"Lustig","year":"2007","journal-title":"Magn Reson Med"},{"key":"10.1016\/j.jmir.2026.102604_bib0002","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20150487","article-title":"Compressed sensing MRI: a review of the clinical literature","volume":"88","author":"Jaspan","year":"2015","journal-title":"Br J Radiol"},{"key":"10.1016\/j.jmir.2026.102604_bib0003","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MSP.2007.914728","article-title":"Compressed sensing MRI: a look at how CS can improve on current imaging techniques","volume":"25","author":"Lustig","year":"2008","journal-title":"IEEE Signal Process Mag"},{"key":"10.1016\/j.jmir.2026.102604_bib0004","doi-asserted-by":"crossref","first-page":"3055","DOI":"10.1002\/mrm.26977","article-title":"Learning a variational network for reconstruction of accelerated MRI data","volume":"79","author":"Hammernik","year":"2018","journal-title":"Magn Reson Med"},{"key":"10.1016\/j.jmir.2026.102604_bib0005","first-page":"514","article-title":"Accelerating magnetic resonance imaging via deep learning","volume":"2016","author":"Wang","year":"2016","journal-title":"Proc IEEE Int Symp Biomed Imaging"},{"key":"10.1016\/j.jmir.2026.102604_bib0006","doi-asserted-by":"crossref","first-page":"204825","DOI":"10.1109\/ACCESS.2020.3034287","article-title":"An adaptive intelligence algorithm for undersampled knee MRI reconstruction","volume":"8","author":"Pezzotti","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.jmir.2026.102604_bib0007","first-page":"770","article-title":"Deep residual learning for image recognition","volume":"2016","author":"He","year":"2016","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"10.1016\/j.jmir.2026.102604_bib0008","first-page":"105","article-title":"Photo-realistic single image super-resolution using a generative adversarial network","volume":"2017","author":"Ledig","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"10.1016\/j.jmir.2026.102604_bib0009","first-page":"5967","article-title":"Image-to-image translation with conditional adversarial networks","volume":"2017","author":"Isola","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"10.1016\/j.jmir.2026.102604_bib0010","doi-asserted-by":"crossref","first-page":"7237","DOI":"10.21037\/qims-24-722","article-title":"Accelerating brain three-dimensional T2 fluid-attenuated inversion recovery using artificial intelligence-assisted compressed sensing: a comparison study with parallel imaging","volume":"14","author":"Ding","year":"2024","journal-title":"Quant Imaging Med Surg"},{"key":"10.1016\/j.jmir.2026.102604_bib0011","doi-asserted-by":"crossref","DOI":"10.1186\/s41747-023-00377-2","article-title":"Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers","volume":"7","author":"Dratsch","year":"2023","journal-title":"Eur Radiol Exp"},{"key":"10.1016\/j.jmir.2026.102604_bib0012","doi-asserted-by":"crossref","first-page":"418","DOI":"10.3390\/diagnostics13030418","article-title":"Conventional and deep-learning-based image reconstructions of undersampled k-space data of the lumbar spine using compressed sensing in MRI: a comparative study on 20 subjects","volume":"13","author":"Fervers","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.jmir.2026.102604_bib0013","doi-asserted-by":"crossref","first-page":"257","DOI":"10.2147\/JPR.S388219","article-title":"Comparison of artificial intelligence-assisted compressed sensing (ACS) and routine two-dimensional sequences on lumbar spine imaging","volume":"Volume 16","author":"Sui","year":"2023","journal-title":"J Pain Res"},{"key":"10.1016\/j.jmir.2026.102604_bib0014","doi-asserted-by":"crossref","DOI":"10.1186\/s41747-024-00446-0","article-title":"Reconstruction of 3D knee MRI using deep learning and compressed sensing: a validation study on healthy volunteers","volume":"8","author":"Dratsch","year":"2024","journal-title":"Eur Radiol Exp"},{"key":"10.1016\/j.jmir.2026.102604_bib0015","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1259\/bjr.20220074","article-title":"A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers","volume":"96","author":"Iuga","year":"2023","journal-title":"Br J Radiol"},{"key":"10.1016\/j.jmir.2026.102604_bib0016","doi-asserted-by":"crossref","first-page":"3046","DOI":"10.1007\/s00330-023-10368-x","article-title":"Application research of AI-assisted compressed sensing technology in MRI scanning of the knee joint: 3D-MRI perspective","volume":"34","author":"Ni","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jmir.2026.102604_bib0017","doi-asserted-by":"crossref","first-page":"8376","DOI":"10.1007\/s00330-022-08919-9","article-title":"Deep learning-based acceleration of compressed sense MR imaging of the ankle","volume":"32","author":"Foreman","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.jmir.2026.102604_bib0018","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1186\/s41747-023-00374-5","article-title":"AI-assisted accelerated MRI of the ankle: clinical practice assessment","volume":"7","author":"Zhao","year":"2023","journal-title":"Eur Radiol Exp"},{"key":"10.1016\/j.jmir.2026.102604_bib0019","first-page":"79","article-title":"Machine learning in magnetic resonance imaging: image reconstruction","volume":"83","author":"Montalt-Tordera","year":"2021","journal-title":"Eur J Med Phys"},{"key":"10.1016\/j.jmir.2026.102604_bib0020","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/0730-725X(88)90450-X","article-title":"EEC concerted research project. IV. Protocols and test objects for the assessment of MRI equipment","volume":"6","year":"1988","journal-title":"Magn Reson Imaging"},{"key":"10.1016\/j.jmir.2026.102604_bib0021","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J Chiropr Med"}],"container-title":["Journal of Medical Imaging and Radiation Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1939865426004182?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1939865426004182?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T20:01:20Z","timestamp":1790452880000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1939865426004182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":21,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,12]]}},"alternative-id":["S1939865426004182"],"URL":"https:\/\/doi.org\/10.1016\/j.jmir.2026.102604","relation":{},"ISSN":["1939-8654"],"issn-type":[{"value":"1939-8654","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Feasibility and image quality evaluation of accelerated T2-weighted and time-of-flight MRI using AI-assisted compressed sensing and deep reconstruction","name":"articletitle","label":"Article Title"},{"value":"Journal of Medical Imaging and Radiation Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jmir.2026.102604","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc. on behalf of Canadian Association of Medical Radiation Technologists.","name":"copyright","label":"Copyright"}],"article-number":"102604"}}