{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T23:12:10Z","timestamp":1777936330976,"version":"3.51.4"},"reference-count":26,"publisher":"Japanese Society for Magnetic Resonance in Medicine","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MRMS","magn reson med sci"],"published-print":{"date-parts":[[2026]]},"DOI":"10.2463\/mrms.mp.2025-0177","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T22:08:28Z","timestamp":1773871708000},"page":"n\/a","source":"Crossref","is-referenced-by-count":0,"title":["Visualization of the Trochlear Nerve Using Deep Learning\u2013enhanced 3D T2-weighted MR Imaging at 3T"],"prefix":"10.2463","volume":"25","author":[{"given":"Taiki","family":"Koshiishi","sequence":"first","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6567-6736","authenticated-orcid":false,"given":"Satoru","family":"Ide","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Occupational and Environmental Health, School of Medicine, Kitakyushu, Fukuoka, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuka","family":"Ishimoto","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomohiro","family":"Shintaku","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sera","family":"Kasai","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jusei","family":"Kudo","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keita","family":"Watanabe","sequence":"additional","affiliation":[{"name":"Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Kyoto, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tetsuya","family":"Wakayama","sequence":"additional","affiliation":[{"name":"GE HealthCare, Hino, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atsushi","family":"Nozaki","sequence":"additional","affiliation":[{"name":"GE HealthCare, Hino, Tokyo, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xucheng","family":"Zhu","sequence":"additional","affiliation":[{"name":"GE HealthCare, Menlo Park CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kana","family":"Saito","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mizuki","family":"Imura","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amo","family":"Ozawa","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuichi","family":"Matsuhashi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatsuro","family":"Sasaki","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saaya","family":"Mori","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masashi","family":"Matsuzaka","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Hirosaki University Hospital, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shingo","family":"Kakeda","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hirosaki University Graduate School of Medicine, Hirosaki Aomori, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1295","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"1. Yasaka K, Kanzawa J, Nakaya M, et al. Super-resolution deep learning reconstruction for 3D brain MR imaging: Improvement of cranial nerve depiction and interobserver agreement in evaluations of neurovascular conflict. Acad Radiol 2024; 31:5118\u20135127.","DOI":"10.1016\/j.acra.2024.06.010"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2. Grams AE, Kraff O, Kalkmann J, et al. Magnetic resonance imaging of cranial nerves at 7 Tesla. Clin Neuroradiol 2013; 23:17\u201323.","DOI":"10.1007\/s00062-012-0144-3"},{"key":"3","unstructured":"3. Casselman JW, Kuhweide R, Deimling M, Ampe W, Dehaene I, Meeus L. Constructive interference in steady state-3DFT MR imaging of the inner ear and cerebellopontine angle. AJNR Am J Neuroradiol 1993; 14:47\u201357."},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Cheng Y-S, Zhou Z-R, Peng W-J, Tang F. Three-dimensional-fast imaging employing steady-state acquisition and T2-weighted fast spin-echo magnetic resonance sequences on visualization of cranial nerves III - XII. Chin Med J (Engl) 2008; 121:276\u2013279.","DOI":"10.1097\/00029330-200802010-00020"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Sheth S, Branstetter BF 4th, Escott EJ. Appearance of normal cranial nerves on steady-state free precession MR images. Radiographics 2009; 29:1045\u20131055.","DOI":"10.1148\/rg.294085743"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Ide S, Murakami Y, Futatsuya K, et al. Usefulness of olfactory bulb measurement in 3D-FIESTA in differentiating Parkinson disease from atypical parkinsonism. AJNR Am J Neuroradiol 2024; 45:1141\u20131152.","DOI":"10.3174\/ajnr.A8275"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Scheffler K, Lehnhardt S. Principles and applications of balanced SSFP techniques. Eur Radiol 2003; 13:2409\u20132418.","DOI":"10.1007\/s00330-003-1957-x"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. Kumar T, Virador GM, Brahmbhatt P, et al. High-resolution 7T MR imaging of the trochlear nerve. AJNR Am J Neuroradiol 2023; 44:186\u2013191.","DOI":"10.3174\/ajnr.A7774"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Ogura A, Maeda F, Miyai A, Kikumoto R. Effects of slice thickness and matrix size on MRI for signal detection. Nihon Hoshasen Gijutsu Gakkai Zasshi 2005; 61:1140\u20131143. (in Japanese)","DOI":"10.6009\/jjrt.KJ00003943075"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: A primer for radiologists. Radiographics 2017; 37:2113\u20132131.","DOI":"10.1148\/rg.2017170077"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Jiang D, Dou W, Vosters L, Xu X, Sun Y, Tan T. Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network. Jpn J Radiol 2018; 36:566\u2013574.","DOI":"10.1007\/s11604-018-0758-8"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12. Kidoh M, Shinoda K, Kitajima M, 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.","DOI":"10.2463\/mrms.mp.2019-0018"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Iwamura M, Ide S, Sato K, et al. Thin-slice two-dimensional T2-weighted imaging with deep learning-based reconstruction: Improved lesion detection in the brain of patients with multiple sclerosis. Magn Reson Med Sci 2024; 23:184\u2013192.","DOI":"10.2463\/mrms.mp.2022-0112"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Ishimoto Y, Ide S, Watanabe K, et al. Usefulness of pituitary high-resolution 3D MRI with deep-learning-based reconstruction for perioperative evaluation of pituitary adenomas. Neuroradiology 2024; 66:937\u2013945.","DOI":"10.1007\/s00234-024-03315-0"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Villain M, Segnarbieux F, Bonnel F, Aubry I, Arnaud B. The trochlear nerve: Anatomy by microdissection. Surg Radiol Anat 1993; 15:169\u2013173.","DOI":"10.1007\/BF01627696"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Choi BS, Kim JH, Jung C, Hwang J-M. High-resolution 3D MR imaging of the trochlear nerve. AJNR Am J Neuroradiol 2010; 31:1076\u20131079.","DOI":"10.3174\/ajnr.A1992"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Tubbs RS, Oakes WJ. Relationships of the cisternal segment of the trochlear nerve. J Neurosurg 1998; 89:1015\u20131019.","DOI":"10.3171\/jns.1998.89.6.1015"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Yousry I, Camelio S, Schmid UD, et al. Visualization of cranial nerves I-XII: Value of 3D CISS and T2-weighted FSE sequences. Eur Radiol 2000; 10:1061\u20131067.","DOI":"10.1007\/s003300000452"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Fischbach F, M\u00fcller M, Bruhn H. High-resolution depiction of the cranial nerves in the posterior fossa (N III-N XII) with 2D fast spin echo and 3D gradient echo sequences at 3.0 T. Clin Imaging 2009; 33:169\u2013174.","DOI":"10.1016\/j.clinimag.2008.09.012"},{"key":"20","unstructured":"20. Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. arXiv:2008.06559 [eess.IV]."},{"key":"21","doi-asserted-by":"crossref","unstructured":"21. McHugh ML. Interrater reliability: The kappa statistic. Biochem Med (Zagreb) 2012; 22:276\u2013282.","DOI":"10.11613\/BM.2012.031"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Vaughan JT, Garwood M, Collins CM, et al. 7T vs. 4T: RF power, homogeneity, and signal-to-noise comparison in head images. Magn Reson Med 2001; 46:24\u201330.","DOI":"10.1002\/mrm.1156"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23. Trattnig S, Springer E, Bogner W, et al. Key clinical benefits of neuroimaging at 7T. Neuroimage 2018; 168:477\u2013489.","DOI":"10.1016\/j.neuroimage.2016.11.031"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Kiryu S, Akai H, Yasaka K, et al. Clinical impact of deep learning reconstruction in MRI. Radiographics 2023; 43:e220133.","DOI":"10.1148\/rg.220133"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Gassenmaier S, Afat S, Nickel D, Mostapha M, Herrmann J, Othman AE. Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality. Eur J Radiol 2021; 137:109600.","DOI":"10.1016\/j.ejrad.2021.109600"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Yoo H, Yoo R-E, Choi SH, et al. Deep learning-based reconstruction for acceleration of lumbar spine MRI: A prospective comparison with standard MRI. Eur Radiol 2023; 33:8656\u20138668.","DOI":"10.1007\/s00330-023-09918-0"}],"container-title":["Magnetic Resonance in Medical Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/mrms\/25\/2\/25_mp.2025-0177\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T03:27:34Z","timestamp":1777692454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/mrms\/25\/2\/25_mp.2025-0177\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":26,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.2463\/mrms.mp.2025-0177","relation":{},"ISSN":["1347-3182","1880-2206"],"issn-type":[{"value":"1347-3182","type":"print"},{"value":"1880-2206","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"article-number":"mp.2025-0177"}}