{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T16:54:03Z","timestamp":1793638443830,"version":"4.5.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002424","name":"Fujifilm Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002424","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP22K15837"],"award-info":[{"award-number":["JP22K15837"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Jpn J Radiol"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Purpose<\/jats:title>\n            <jats:p>To evaluate deep learning-reconstructed (DLR)\u2013fluid-attenuated inversion recovery (FLAIR) images generated from undersampled data, compare them with fully sampled and rapidly acquired FLAIR images, and assess their potential for white matter hyperintensity evaluation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>We examined 30 patients with white matter hyperintensities, obtaining fully sampled FLAIR images (standard FLAIR, std-FLAIR). We created accelerated FLAIR (acc-FLAIR) images using one-third of the fully sampled data and applied deep learning to generate DLR\u2013FLAIR images. Three neuroradiologists assessed the quality (amount of noise and gray\/white matter contrast) in all three image types. The reproducibility of hyperintensities was evaluated by comparing a subset of 100 hyperintensities in acc-FLAIR and DLR\u2013FLAIR images with those in the std-FLAIR images. Quantitatively, similarities and errors of the entire image and the focused regions on white matter hyperintensities in acc-FLAIR and DLR\u2013FLAIR images were measured against std-FLAIR images using structural similarity index measure (SSIM), regional SSIM, normalized root mean square error (NRMSE), and regional NRMSE values.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>All three neuroradiologists evaluated DLR\u2013FLAIR as having significantly less noise and higher image quality scores compared with std-FLAIR and acc-FLAIR (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001). All three neuroradiologists assigned significantly higher frontal lobe gray\/white matter visibility scores for DLR\u2013FLAIR than for acc-FLAIR (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.001); two neuroradiologists attributed significantly higher scores for DLR\u2013FLAIR than for std-FLAIR (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). Regarding white matter hyperintensities, all three neuroradiologists significantly preferred DLR\u2013FLAIR (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001). DLR\u2013FLAIR exhibited higher similarity to std-FLAIR in terms of visibility of the hyperintensities, with 97% of the hyperintensities rated as nearly identical or equivalent. Quantitatively, DLR\u2013FLAIR demonstrated significantly higher SSIM and regional SSIM values than acc-FLAIR, with significantly lower NRMSE and regional NRMSE values (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>DLR\u2013FLAIR can reduce scan time and generate images of similar quality to std-FLAIR in patients with white matter hyperintensities. Therefore, DLR\u2013FLAIR may serve as an effective method in traditional magnetic resonance imaging protocols.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11604-024-01666-5","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T05:48:59Z","timestamp":1727156939000},"page":"200-209","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Accelerating FLAIR imaging via deep learning reconstruction: potential for evaluating white matter hyperintensities"],"prefix":"10.1007","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9954-3838","authenticated-orcid":false,"given":"Noriko","family":"Nishioka","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6070-3766","authenticated-orcid":false,"given":"Yukie","family":"Shimizu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yukio","family":"Kaneko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toru","family":"Shirai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atsuro","family":"Suzuki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoki","family":"Amemiya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hisaaki","family":"Ochi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshitaka","family":"Bito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masahiro","family":"Takizawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yohei","family":"Ikebe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Kameda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taisuke","family":"Harada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriyuki","family":"Fujima","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kohsuke","family":"Kudo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"key":"1666_CR1","doi-asserted-by":"publisher","unstructured":"Sati P, George IC, Shea CD, Gait\u00e1n MI, Reich DS, FLAIR. FLAIR*: A combined MR contrast technique for visualizing white matter lesions and parenchymal veins. Radiology. 2012;265:926\u201332. https:\/\/doi.org\/10.1148\/radiol.12120208.","DOI":"10.1148\/radiol.12120208"},{"key":"1666_CR2","first-page":"455","volume":"27","author":"JH Simon","year":"2006","unstructured":"Simon JH, Li D, Traboulsee A, Coyle PK, Arnold DL, Barkhof F, et al. Standardized MR imaging protocol for multiple sclerosis: Consortium of MS Centers consensus guidelines. AJNR Am J Neuroradiol. 2006;27:455\u201361.\u00a0https:\/\/www.ajnr.org\/content\/27\/2\/455.long","journal-title":"AJNR Am J Neuroradiol"},{"key":"1666_CR3","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1002\/jmri.25737","volume":"46","author":"M Saranathan","year":"2017","unstructured":"Saranathan M, Worters PW, Rettmann DW, Winegar B, Becker J. Physics for clinicians: fluid-attenuated inversion recovery (FLAIR) and double inversion recovery (DIR) Imaging. J Magn Reson Imaging. 2017;46:1590\u2013600. https:\/\/doi.org\/10.1002\/jmri.25737.","journal-title":"J Magn Reson Imaging"},{"key":"1666_CR4","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.mri.2013.12.001","volume":"32","author":"S Aja-Fern\u00e1ndez","year":"2014","unstructured":"Aja-Fern\u00e1ndez S, Vegas-S\u00e1nchez-Ferrero G, Trist\u00e1n-Vega A. Noise estimation in parallel MRI: GRAPPA and SENSE. Magn Reson Imaging. 2014;32:281\u201390. https:\/\/doi.org\/10.1016\/j.mri.2013.12.001.","journal-title":"Magn Reson Imaging"},{"key":"1666_CR5","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1109\/IEMBS.2006.259697","volume":"2006","author":"WS Hoge","year":"2006","unstructured":"Hoge WS, Brooks DH. On the complimentarity of SENSE and GRAPPA in parallel MR imaging. Conf Proc IEEE Eng Med Biol Soc. 2006;2006:755\u20138. https:\/\/doi.org\/10.1109\/IEMBS.2006.259697.","journal-title":"Conf Proc IEEE Eng Med Biol Soc"},{"key":"1666_CR6","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1002\/mrm.21391","volume":"58","author":"M Lustig","year":"2007","unstructured":"Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med. 2007;58:1182\u201395. https:\/\/doi.org\/10.1002\/mrm.21391.","journal-title":"Magn Reson Med"},{"key":"1666_CR7","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1002\/mrm.22161","volume":"62","author":"D Liang","year":"2009","unstructured":"Liang D, Liu B, Wang J, Ying L. Accelerating SENSE using compressed sensing. Magn Reson Med. 2009;62:1574\u201384. https:\/\/doi.org\/10.1002\/mrm.22161.","journal-title":"Magn Reson Med"},{"key":"1666_CR8","doi-asserted-by":"publisher","first-page":"20150487","DOI":"10.1259\/bjr.20150487","volume":"88","author":"ON Jaspan","year":"2015","unstructured":"Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: a review of the clinical literature. Br J Radiol. 2015;88:20150487. https:\/\/doi.org\/10.1259\/bjr.20150487.","journal-title":"Br J Radiol"},{"key":"1666_CR9","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.mri.2008.10.008","volume":"27","author":"Y Qian","year":"2009","unstructured":"Qian Y, Stenger VA, Boada FE. Parallel imaging with 3D TPI trajectory: SNR and acceleration benefits. Magn Reson Imaging. 2009;27:656\u201363. https:\/\/doi.org\/10.1016\/j.mri.2008.10.008.","journal-title":"Magn Reson Imaging"},{"key":"1666_CR10","doi-asserted-by":"publisher","DOI":"10.1155\/2012\/864827","volume":"2012","author":"DS Smith","year":"2012","unstructured":"Smith DS, Gore JC, Yankeelov TE, Welch EB. Real-time compressive sensing MRI reconstruction using GPU computing and split Bregman methods. Int J Biomed Imaging. 2012;2012: 864827. https:\/\/doi.org\/10.1155\/2012\/864827.","journal-title":"Int J Biomed Imaging"},{"key":"1666_CR11","doi-asserted-by":"publisher","DOI":"10.1148\/rg.220133","volume":"43","author":"S Kiryu","year":"2023","unstructured":"Kiryu S, Akai H, Yasaka K, Tajima T, Kunimatsu A, Yoshioka N, et al. Clinical impact of deep learning reconstruction in MRI. Radiographics. 2023;43: e220133. https:\/\/doi.org\/10.1148\/rg.220133.","journal-title":"Radiographics"},{"key":"1666_CR12","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1007\/s11604-023-01470-7","volume":"41","author":"N Hamabuchi","year":"2023","unstructured":"Hamabuchi N, Ohno Y, Kimata H, Ito Y, Fujii K, Akino N, et al. Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images. Jpn J Radiol. 2023;41:1373\u201388. https:\/\/doi.org\/10.1007\/s11604-023-01470-7.","journal-title":"Jpn J Radiol"},{"key":"1666_CR13","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1007\/s11604-023-01402-5","volume":"41","author":"R Hosoi","year":"2023","unstructured":"Hosoi R, Yasaka K, Mizuki M, Yamaguchi H, Miyo R, Hamada A, et al. Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses. Jpn J Radiol. 2023;41:863\u201371. https:\/\/doi.org\/10.1007\/s11604-023-01402-5.","journal-title":"Jpn J Radiol"},{"key":"1666_CR14","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1007\/s11604-023-01452-9","volume":"41","author":"S Oshima","year":"2023","unstructured":"Oshima S, Fushimi Y, Miyake KK, Nakajima S, Sakata A, Okuchi S, et al. Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance. Jpn J Radiol. 2023;41:1216\u201325. https:\/\/doi.org\/10.1007\/s11604-023-01452-9.","journal-title":"Jpn J Radiol"},{"key":"1666_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s11604-024-01614-3","author":"Y Tanahashi","year":"2024","unstructured":"Tanahashi Y, Kubota K, Nomura T, Ikeda T, Kutsuna M, Funayama S, et al. Improved vascular depiction and image quality through deep learning reconstruction of CT hepatic arteriography during transcatheter arterial chemoembolization. Jpn J Radiol. 2024. https:\/\/doi.org\/10.1007\/s11604-024-01614-3.","journal-title":"Jpn J Radiol"},{"key":"1666_CR16","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s00234-022-03053-1","volume":"65","author":"SH Kim","year":"2023","unstructured":"Kim SH, Choi YH, Lee JS, Lee SB, Cho YJ, Lee SH, et al. Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI. Neuroradiology. 2023;65:207\u201314. https:\/\/doi.org\/10.1007\/s00234-022-03053-1.","journal-title":"Neuroradiology"},{"key":"1666_CR17","doi-asserted-by":"publisher","first-page":"2130","DOI":"10.3174\/ajnr.A7358","volume":"42","author":"S Bash","year":"2021","unstructured":"Bash S, Wang L, Airriess C, Zaharchuk G, Gong E, Shankaranarayanan A, et al. Deep learning enables 60% accelerated volumetric brain MRI while preserving quantitative performance: a prospective, multicenter, multireader trial. AJNR Am J Neuroradiol. 2021;42:2130\u20137. https:\/\/doi.org\/10.3174\/ajnr.A7358.","journal-title":"AJNR Am J Neuroradiol"},{"key":"1666_CR18","doi-asserted-by":"publisher","first-page":"195","DOI":"10.2463\/mrms.mp.2019-0018","volume":"19","author":"M Kidoh","year":"2020","unstructured":"Kidoh M, Shinoda K, Kitajima M, Isogawa K, Nambu M, Uetani H, et al. Deep learning based noise reduction for brain MR imaging: Tests on phantoms and healthy volunteers. Magn Reson Med Sci. 2020;19:195\u2013206. https:\/\/doi.org\/10.2463\/mrms.mp.2019-0018.","journal-title":"Magn Reson Med Sci"},{"key":"1666_CR19","doi-asserted-by":"publisher","first-page":"e13","DOI":"10.1016\/j.crad.2022.08.127","volume":"78","author":"T Tajima","year":"2023","unstructured":"Tajima T, Akai H, Yasaka K, Kunimatsu A, Yamashita Y, Akahane M, et al. Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images. Clin Radiol. 2023;78:e13-21. https:\/\/doi.org\/10.1016\/j.crad.2022.08.127.","journal-title":"Clin Radiol."},{"key":"1666_CR20","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.acra.2023.05.010","volume":"31","author":"A Estler","year":"2024","unstructured":"Estler A, Hauser TK, Mengel A, Brunn\u00e9e M, Zerweck L, Richter V, et al. Deep learning accelerated image reconstruction of fluid-attenuated inversion recovery sequence in brain imaging: reduction of acquisition time and improvement of image quality. Acad Radiol. 2024;31:180\u20136. https:\/\/doi.org\/10.1016\/j.acra.2023.05.010.","journal-title":"Acad Radiol"},{"key":"1666_CR21","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.3174\/ajnr.A7589","volume":"43","author":"T Yamamoto","year":"2022","unstructured":"Yamamoto T, Lacheret C, Fukutomi H, Kamraoui RA, Denat L, Zhang B, et al. Validation of a denoising method using deep learning-based reconstruction to quantify multiple sclerosis lesion load on fast FLAIR imaging. AJNR Am J Neuroradiol. 2022;43:1099\u2013106. https:\/\/doi.org\/10.3174\/ajnr.A7589.","journal-title":"AJNR Am J Neuroradiol"},{"key":"1666_CR22","first-page":"37","volume":"41","author":"A Suzuki","year":"2023","unstructured":"Suzuki A, Amemiya T, Kaneko Y, Shirai T. Combination of iterative reconstruction and CNN-based denoising for non-uniform noise for parallel imaging in MRI. Med Imaging Technol. 2023;41:37\u201351.","journal-title":"Med Imaging Technol"},{"key":"1666_CR23","doi-asserted-by":"publisher","first-page":"1397","DOI":"10.1002\/mrm.20285","volume":"52","author":"AA Samsonov","year":"2004","unstructured":"Samsonov AA, Kholmovski EG, Parker DL, Johnson CR. POCSENSE: POCS-based reconstruction for sensitivity encoded magnetic resonance imaging. Magn Reson Med. 2004;52:1397\u2013406. https:\/\/doi.org\/10.1002\/mrm.20285.","journal-title":"Magn Reson Med"},{"key":"1666_CR24","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","volume":"38","author":"C Dong","year":"2016","unstructured":"Dong C, Loy CC, He K, Tang X. Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell. 2016;38:295\u2013307. https:\/\/doi.org\/10.1109\/TPAMI.2015.2439281.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1666_CR25","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","volume":"2016","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016;2016:770\u20138. https:\/\/doi.org\/10.1109\/CVPR.2016.90.","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"1666_CR26","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/tip.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: From error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600\u201312. https:\/\/doi.org\/10.1109\/tip.2003.819861.","journal-title":"IEEE Trans Image Process"},{"key":"1666_CR27","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15:155\u201363. https:\/\/doi.org\/10.1016\/j.jcm.2016.02.012.","journal-title":"J Chiropr Med"}],"container-title":["Japanese Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-024-01666-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11604-024-01666-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11604-024-01666-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T06:08:52Z","timestamp":1738562932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11604-024-01666-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,24]]},"references-count":27,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["1666"],"URL":"https:\/\/doi.org\/10.1007\/s11604-024-01666-5","relation":{},"ISSN":["1867-1071","1867-108X"],"issn-type":[{"value":"1867-1071","type":"print"},{"value":"1867-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,24]]},"assertion":[{"value":"28 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors of this manuscript declare relationships with the following companies: Yukio Kaneko, Toru Shirai, Atsuro Suzuki, Tomoki Amemiya, and Hisaaki Ochi are currently employed by the FUJIFILM Corporation. Yoshitaka Bito was employed by FUJIFILM Healthcare Corporation. Masahiro Takizawa is employed by FUJIFILM Healthcare Corporation. Kohsuke Kudo received funding from FUJIFILM Healthcare Corporation for this research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}},{"value":"The Institutional Review Board (IRB) of our hospital approved this retrospective study (ID: 019\u20130192), and the requirement for informed consent was waived. This study was conducted in accordance with the ethical standards of the IRB as well as the tenets of the 1964 Helsinki Declaration and its later amendments.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical statement"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}