{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T05:54:24Z","timestamp":1791006864679,"version":"4.1.0"},"reference-count":39,"publisher":"Japanese Society for Magnetic Resonance in Medicine","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MRMS","magn reson med sci"],"published-print":{"date-parts":[[2026]]},"DOI":"10.2463\/mrms.mp.2026-0090","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T22:12:11Z","timestamp":1790892731000},"page":"n\/a","source":"Crossref","is-referenced-by-count":0,"title":["Effects of Artificial Intelligence\u2013based Reconstruction on Image Quality and Voxel-based Morphometry Analysis of Atrophy in Accelerated MPRAGE"],"prefix":"10.2463","volume":"25","author":[{"given":"Tatsunori","family":"Horie","sequence":"first","affiliation":[{"name":"Department of Radiological Technology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9021-347X","authenticated-orcid":false,"given":"Noriyuki","family":"Fujima","sequence":"additional","affiliation":[{"name":"Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Kameda","sequence":"additional","affiliation":[{"name":"Faculty of Dental Medicine, Department of Radiology, Hokkaido University, Sapporo, Hokkaido, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamotsu","family":"Kamishima","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, Hokkaido University, Sapporo, Hokkaido, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kohsuke","family":"Kudo","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan"},{"name":"Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1295","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"1. Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): Fast imaging with radiofrequency coil arrays. Magn Reson Med 1997; 38:591\u2013603.","DOI":"10.1002\/mrm.1910380414"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2. Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI. Magn Reson Med 1999; 42:952\u2013962.","DOI":"10.1002\/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S"},{"key":"3","doi-asserted-by":"crossref","unstructured":"3. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002; 47:1202\u20131210.","DOI":"10.1002\/mrm.10171"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Heidemann RM, \u00d6zsarlak \u00d6, Parizel PM, et al. A brief review of parallel magnetic resonance imaging. Eur Radiol 2003; 13:2323\u20132337.","DOI":"10.1007\/s00330-003-1992-7"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Blaimer M, Breuer F, Mueller M, Heidemann RM, Griswold MA, Jakob PM. SMASH, SENSE PILS, GRAPPA: How to choose the optimal method. Top Magn Reson Imaging 2004; 15:223\u2013236.","DOI":"10.1097\/01.rmr.0000136558.09801.dd"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Larkman DJ, Nunes RG. Parallel magnetic resonance imaging. Phys Med Biol 2007; 52:R15\u2013R55.","DOI":"10.1088\/0031-9155\/52\/7\/R01"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Katscher U, B\u00f6rnert P. Parallel magnetic resonance imaging. Neurotherapeutics 2007; 4:499\u2013510.","DOI":"10.1016\/j.nurt.2007.04.011"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58:1182\u20131195.","DOI":"10.1002\/mrm.21391"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Lustig M, Donoho DL, Santos JM, Pauly JM. Compressed sensing MRI. IEEE Signal Process Mag 2008; 25:72\u201382.","DOI":"10.1109\/MSP.2007.914728"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Sharma SD, Fong CL, Tzung BS, Law M, Nayak KS. Clinical image quality assessment of accelerated magnetic resonance neuroimaging using compressed sensing. Invest Radiol 2013; 48:638\u2013645.","DOI":"10.1097\/RLI.0b013e31828a012d"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Kayvanrad M, Lin A, Joshi R, Chiu J, Peters T. Diagnostic quality assessment of compressed sensing accelerated magnetic resonance neuroimaging. J Magn Reson Imaging 2016; 44:433\u2013444.","DOI":"10.1002\/jmri.25149"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12. Dieckmeyer M, Roy AG, Senapati J, et al. Effect of MRI acquisition acceleration via compressed sensing and parallel imaging on brain volumetry. MAGMA 2021; 34:487\u2013497.","DOI":"10.1007\/s10334-020-00906-9"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Nikoli\u0107 O, Nikoli\u0107 MB, Panteli\u0107 M, et al. Compressed SENSE acceleration factor influence on magnetic resonance image quality in patients with endometrial cancer. Phys Med 2025; 130:104899.","DOI":"10.1016\/j.ejmp.2025.104899"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Wang S, Su Z, Ying L, et al. Accelerating magnetic resonance imaging via deep learning. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016; 514-517.","DOI":"10.1109\/ISBI.2016.7493320"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 2018; 79:3055\u20133071.","DOI":"10.1002\/mrm.26977"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Knoll F, Hammernik K, Zhang C, et al. Deep-Learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues. IEEE Signal Process Mag 2020; 37:128\u2013140.","DOI":"10.1109\/MSP.2019.2950640"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Kim S, Park H, Park S-H. A review of deep learning-based reconstruction methods for accelerated MRI using spatiotemporal and multi-contrast redundancies. Biomed Eng Lett 2024; 14:1221\u20131242.","DOI":"10.1007\/s13534-024-00425-9"},{"key":"18","unstructured":"18. Pezzotti N, de Weerdt E, Yousefi S, et al. Adaptive-CS-net: FastMRI with adaptive intelligence. arXiv 2019;1912.12259."},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Pezzotti N, Yousefi S, Elmahdy MS, et al. An adaptive intelligence algorithm for undersampled knee MRI reconstruction. IEEE Access 2020; 8:204825-204838.","DOI":"10.1109\/ACCESS.2020.3034287"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20. 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":"21","doi-asserted-by":"crossref","unstructured":"21. Wang X, Ma J, Bhosale P, et al. Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging. Abdom Radiol (NY) 2021; 46:3378\u20133386.","DOI":"10.1007\/s00261-021-02964-6"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Tajima T, Akai H, Sugawara H, et al. Feasibility of accelerated whole-body diffusion-weighted imaging using a deep learning-based noise-reduction technique in patients with prostate cancer. Magn Reson Imaging 2022; 92:169\u2013179.","DOI":"10.1016\/j.mri.2022.06.014"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23. Rudie JD, Gleason T, Barkovich MJ, et al. Clinical assessment of deep learning\u2013based super-resolution for 3D volumetric brain MRI. Radiol Artif Intell 2022; 4:e210059.","DOI":"10.1148\/ryai.210059"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Wessling D, Herrmann J, Afat S, et al. Application of a deep learning algorithm for combined super-resolution and partial Fourier reconstruction including time reduction in T1-weighted precontrast and postcontrast gradient echo imaging of abdominopelvic MR imaging. Diagnostics (Basel) 2022; 12:2370.","DOI":"10.3390\/diagnostics12102370"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Feuerriegel GC, Weiss K, Kronthaler S, et al. Evaluation of a deep learning-based reconstruction method for denoising and image enhancement of shoulder MRI in patients with shoulder pain. Eur Radiol 2023; 33:4875\u20134884.","DOI":"10.1007\/s00330-023-09472-9"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Hirano Y, Fujima N, Kameda H, et al. High resolution TOF-MRA using compressed sensing-based deep learning image reconstruction for the visualization of lenticulostriate arteries: A preliminary study. Magn Reson Med Sci 2025; 24:2024-0025.","DOI":"10.2463\/mrms.mp.2024-0025"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Fujita S, Nickel D, Lo W-C, et al. Physics-informed deep learning reconstruction for ultrafast clinical 3D fluid-attenuated inversion recovery brain MRI. Radiol Adv 2025; 2:umaf016.","DOI":"10.1093\/radadv\/umaf016"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. Mugler JP 3rd, Brookeman JR. Three\u2010dimensional magnetization\u2010prepared rapid gradient\u2010echo imaging (3D MP RAGE). Magn Reson Med 1990; 15:152\u2013157.","DOI":"10.1002\/mrm.1910150117"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29. Fox NC, Warrington EK, Freeborough PA, et al. Presymptomatic hippocampal atrophy in Alzheimer\u2019s disease. Brain 1996; 119:2001\u20132007.","DOI":"10.1093\/brain\/119.6.2001"},{"key":"30","doi-asserted-by":"crossref","unstructured":"30. Schuff N, Woerner N, Boreta L, et al. MRI of hippocampal volume loss in early Alzheimer\u2019s disease in relation to ApoE genotype and biomarkers. Brain 2009; 132:1067\u20131077.","DOI":"10.1093\/brain\/awp007"},{"key":"31","doi-asserted-by":"crossref","unstructured":"31. Henneman WJP, Sluimer JD, Barnes J, et al. Hippocampal atrophy rates in Alzheimer disease: Added value over whole brain volume measures. Neurology 2009; 72:999\u20131007.","DOI":"10.1212\/01.wnl.0000344568.09360.31"},{"key":"32","doi-asserted-by":"crossref","unstructured":"32. Vemuri P, Jack CR Jr Role of structural MRI in Alzheimer\u2019s disease. Alzheimers Res Ther 2010; 2:23.","DOI":"10.1186\/alzrt47"},{"key":"33","unstructured":"33. Matsuda H. Voxel-based morphometry of brain MRI in normal aging and Alzheimer\u2019s disease. Aging Dis 2013; 4:29\u201337"},{"key":"34","doi-asserted-by":"crossref","unstructured":"34. Buchert R, Suppa P, Ardekani BA, et al. Easy-to-use and easy-to-interpret quality control of 3D gradient echo T1-weighted MR acquisition sequences for improved test-retest stability of MRI-based hippocampus volumetry. J Alzheimers Dis 2025; 108:844\u2013861.","DOI":"10.1177\/13872877251380301"},{"key":"35","doi-asserted-by":"crossref","unstructured":"35. Cho SJ, Choi YJ, Chung SR, Lee JH, Baek JH. High-resolution MRI using compressed sensing-sensitivity encoding (CS-SENSE) for patients with suspected neurovascular compression syndrome: Comparison with the conventional SENSE parallel acquisition technique. Clin Radiol 2019; 74:817.e9\u2013817.e14.","DOI":"10.1016\/j.crad.2019.06.023"},{"key":"36","doi-asserted-by":"crossref","unstructured":"36. Nagaraj UD, Dillman JR, Tkach JA, Greer JS, Leach JL. Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain. Neuroradiology 2024; 66:1849\u20131857.","DOI":"10.1007\/s00234-024-03417-9"},{"key":"37","doi-asserted-by":"crossref","unstructured":"37. Watanabe K, Oyu K, Kasai S, et al. Deep-learning reconstruction enables about one minute 3D T1-weighted MRI: Quantitative evaluation of Acceleration-quality Trade-offs and motion reduction. Neuroradiology 2026; 68:1377\u20131385.","DOI":"10.1007\/s00234-025-03846-0"},{"key":"38","doi-asserted-by":"crossref","unstructured":"38. Gil N, Tabari A, Nickel D, et al. Deep-learning-accelerated T1-MPRAGE MRI for quantification and visual grading of cerebral volume in memory loss patients. Radiol Adv 2025; 2:umaf022.","DOI":"10.1093\/radadv\/umaf022"},{"key":"39","doi-asserted-by":"crossref","unstructured":"39. Antun V, Renna F, Poon C, Adcock B, Hansen AC. On instabilities of deep learning in image reconstruction and the potential costs of AI. Proc Natl Acad Sci USA 2020; 117:30088\u201330095.","DOI":"10.1073\/pnas.1907377117"}],"container-title":["Magnetic Resonance in Medical Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/mrms\/25\/4\/25_mp.2026-0090\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T05:25:36Z","timestamp":1791005136000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/mrms\/25\/4\/25_mp.2026-0090\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":39,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.2463\/mrms.mp.2026-0090","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.2026-0090"}}