MR Imaging in the 21st Century: Technical Innovation over the First Two Decades

Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI system component, including data acquisition, image reconstruction, and hardware systems, have impacted the others. Progress in each compone...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Magnetic Resonance in Medical Sciences Jg. 21; H. 1; S. 71 - 82
1. Verfasser:	Kabasawa, Hiroyuki
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Japan Japanese Society for Magnetic Resonance in Medicine 01.01.2022 Japan Science and Technology Agency
Schlagworte:	Data acquisition Data processing Electronics industry Hybrid systems Image acquisition Image processing Image Processing, Computer-Assisted - methods Image reconstruction Innovations Inventions Magnetic resonance imaging Magnetic Resonance Imaging - methods magnetic resonance imaging applications magnetic resonance imaging system Medical imaging New technology Positron emission Positron emission tomography Review Tomography magnetic resonance imaging image acquisition image reconstruction magnetic resonance imaging system magnetic resonance imaging applications
ISSN:	1347-3182, 1880-2206, 1880-2206
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI system component, including data acquisition, image reconstruction, and hardware systems, have impacted the others. Progress in each component has induced new technology development opportunities in other components. New technologies outside of the MRI field, for example, computer science, data processing, and semiconductors, have been immediately incorporated into MRI development, which resulted in innovative applications. With high performance computing and MR technology innovations, MRI can now provide large volumes of functional and anatomical image datasets, which are important tools in various research fields. MRI systems are now combined with other modalities, such as positron emission tomography (PET) or therapeutic devices. These hybrid systems provide additional capabilities.In this review, MRI advances in the last two decades will be considered. We will discuss the progress of MRI systems, the enabling technology, established applications, current trends, and the future outlook.
AbstractList	Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI system component, including data acquisition, image reconstruction, and hardware systems, have impacted the others. Progress in each component has induced new technology development opportunities in other components. New technologies outside of the MRI field, for example, computer science, data processing, and semiconductors, have been immediately incorporated into MRI development, which resulted in innovative applications. With high performance computing and MR technology innovations, MRI can now provide large volumes of functional and anatomical image datasets, which are important tools in various research fields. MRI systems are now combined with other modalities, such as positron emission tomography (PET) or therapeutic devices. These hybrid systems provide additional capabilities.In this review, MRI advances in the last two decades will be considered. We will discuss the progress of MRI systems, the enabling technology, established applications, current trends, and the future outlook.Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI system component, including data acquisition, image reconstruction, and hardware systems, have impacted the others. Progress in each component has induced new technology development opportunities in other components. New technologies outside of the MRI field, for example, computer science, data processing, and semiconductors, have been immediately incorporated into MRI development, which resulted in innovative applications. With high performance computing and MR technology innovations, MRI can now provide large volumes of functional and anatomical image datasets, which are important tools in various research fields. MRI systems are now combined with other modalities, such as positron emission tomography (PET) or therapeutic devices. These hybrid systems provide additional capabilities.In this review, MRI advances in the last two decades will be considered. We will discuss the progress of MRI systems, the enabling technology, established applications, current trends, and the future outlook. Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI system component, including data acquisition, image reconstruction, and hardware systems, have impacted the others. Progress in each component has induced new technology development opportunities in other components. New technologies outside of the MRI field, for example, computer science, data processing, and semiconductors, have been immediately incorporated into MRI development, which resulted in innovative applications. With high performance computing and MR technology innovations, MRI can now provide large volumes of functional and anatomical image datasets, which are important tools in various research fields. MRI systems are now combined with other modalities, such as positron emission tomography (PET) or therapeutic devices. These hybrid systems provide additional capabilities. In this review, MRI advances in the last two decades will be considered. We will discuss the progress of MRI systems, the enabling technology, established applications, current trends, and the future outlook.
ArticleNumber	rev.2021-0011
Author	Kabasawa, Hiroyuki
Author_xml	– sequence: 1 fullname: Kabasawa, Hiroyuki organization: Department of Radiological Sciences, School of Health Sciences at Narita, International University of Health and Welfare
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33867419$$D View this record in MEDLINE/PubMed
BookMark	eNp9kU9r3DAQxUVJaJJtvkAPxdBLL97on22ph0LYNs1CSqBszkJWxrtabCmV5C359pWz6dLk0IskmN-beaN3ho6cd4DQe4LnlNfsYghDnAfYzSmmpMSYkDfolAiBS0pxfZTfjDclI4KeoLMYtxgzkctv0Qljom44kafo9sfPYjnotXXrwroibaCgJKZiAS6N4fFzsQKzcdbovlg653c6We8Kv4PwxF7ZkOHVb198BaPvIb5Dx53uI5w_3zN0d_Vttbgub26_LxeXN6WpBU8l4RKElK3knHTCVJOvrm1lRYBy2QppqJGat5wR1tW0wiAwgKgMEQwMBTZDX_Z9H8Z2gHuT7Qbdq4dgBx0elddWvaw4u1Frv1OSSCkbnht8em4Q_K8RYlKDjQb6XjvwY1S0IhVusMwGZujjK3Trx-DyeorWjAvBGsoy9eFfRwcrf_86A3QPmOBjDNAdEILVFKiaAlU5UDUFqqZAs0i8EhmbnkLIW9n-_9LrvXQbk17DYZoOyZoe9pIMk-l4IT0gZqODAsf-AG0IwNs
CitedBy_id	crossref_primary_10_32604_cmc_2024_049186 crossref_primary_10_3390_brainsci14121196 crossref_primary_10_7759_cureus_76185 crossref_primary_10_1016_j_jplph_2025_154502 crossref_primary_10_3390_diagnostics15080960 crossref_primary_10_1007_s40272_025_00683_9 crossref_primary_10_1016_j_heliyon_2024_e26674 crossref_primary_10_7759_cureus_67432 crossref_primary_10_7759_cureus_67553 crossref_primary_10_3390_brainsci13060912 crossref_primary_10_1002_EXP_20230063 crossref_primary_10_7759_cureus_67591 crossref_primary_10_3390_s25134021 crossref_primary_10_1007_s11547_023_01638_1 crossref_primary_10_1080_20415990_2024_2405456 crossref_primary_10_1177_15357597251328822 crossref_primary_10_1007_s10334_023_01115_w crossref_primary_10_1016_j_ijrefrig_2025_06_037 crossref_primary_10_3390_s25061838 crossref_primary_10_1021_acschemneuro_5c00008 crossref_primary_10_3390_cancers16081478 crossref_primary_10_3390_pharmaceutics15071840 crossref_primary_10_1088_1742_6596_2877_1_012113 crossref_primary_10_1007_s00330_024_11105_8 crossref_primary_10_1002_jmrs_807 crossref_primary_10_1016_j_asmr_2024_101065 crossref_primary_10_1080_09553002_2025_2531903 crossref_primary_10_3390_diagnostics14151647 crossref_primary_10_3390_nano15070542 crossref_primary_10_1016_j_mric_2025_03_001 crossref_primary_10_1097_RLI_0000000000000962 crossref_primary_10_1016_j_yfrne_2024_101174
Cites_doi	10.2463/mrms.4.95 10.3174/ajnr.A5926 10.1007/s11604-007-0169-8 10.2463/mrms.tn.2016-0049 10.1002/jmri.23515 10.1002/jmri.20842 10.1007/s00330-020-06848-z 10.1016/j.mri.2014.08.038 10.1088/1361-6560/aac763 10.2463/mrms.rev.2019-0096 10.2463/mrms.mp.2018-0077 10.2463/mrms.8.149 10.2463/mrms.mp.2019-0137 10.2463/mrms.tn.2019-0172 10.2463/mrms.tn.2017-0103 10.1016/S0899-7071(03)00151-7 10.2463/mrms.11.243 10.2463/mrms.4.83 10.1002/mrm.25213 10.2463/mrms.2014-0004 10.1002/mrm.27134 10.1002/mrm.1910160203 10.2463/mrms.mp-2019-0122 10.1002/mp.14199 10.3174/ajnr.A4199 10.2463/mrms.bc.2020-0046 10.1016/j.ejrad.2016.07.018 10.2463/mrms.tn.2015-0152 10.1002/jmri.22789 10.1056/NEJMoa1600159 10.1038/srep38366 10.2463/mrms.mp.2019-0018 10.1002/1522-2586(200011)12:5<776::AID-JMRI17>3.0.CO;2-X 10.2463/mrms.3.45 10.2463/mrms.8.165 10.1016/j.ejrad.2018.05.033 10.1002/mrm.10171 10.1097/RLI.0000000000000744 10.1002/jmri.26211 10.2463/mrms.mp.2016-0047 10.2463/mrms.mp.2019-0030 10.2463/mrms.mp.2019-0195 10.2463/mrms.2.133 10.1148/ryai.2019180069 10.2463/mrms.2.37 10.2463/mrms.11.291 10.2463/mrms.3.177 10.1002/jmri.25770 10.2463/mrms.2012-0006 10.1002/hbm.25232 10.2463/mrms.rev.2019-0124 10.2463/mrms.tn.2018-0066 10.1007/s11604-016-0584-9 10.2463/mrms.2013-0008 10.1088/1361-6560/aab691 10.1016/j.mri.2009.02.004 10.2463/mrms.mp.2017-0132 10.2463/mrms.11.129 10.1002/jmri.1880010410 10.2463/mrms.mp.2018-0156 10.1002/jmri.25903 10.1002/mrm.25770 10.2463/mrms.2014-0147 10.1016/j.mri.2015.04.004 10.1097/RLI.0000000000000548 10.1002/mrm.24902 10.2463/mrms.mp.2016-0045 10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S 10.2463/mrms.2013-0127 10.2463/mrms.mp.2015-0137 10.2463/mrms.mp.2018-0174 10.2463/mrms.tn.2018-0168 10.1002/mrm.1910380414 10.1097/RLI.0b013e318271869c 10.1007/s11604-018-0787-3 10.2463/mrms.mp.2020-0021 10.2463/mrms.mp.2016-0062 10.2463/mrms.6.29 10.2463/mrms.tn.2016-0069 10.2463/mrms.11.221 10.1002/mrm.21391 10.1002/mrm.22176 10.1002/mrm.23097 10.1002/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L
ContentType	Journal Article
Copyright	2021 by Japanese Society for Magnetic Resonance in Medicine 2022. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 Japanese Society for Magnetic Resonance in Medicine 2021
Copyright_xml	– notice: 2021 by Japanese Society for Magnetic Resonance in Medicine – notice: 2022. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2021 Japanese Society for Magnetic Resonance in Medicine 2021
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QO 7TK 8FD FR3 K9. NAPCQ P64 7X8 5PM
DOI	10.2463/mrms.rev.2021-0011
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Biotechnology Research Abstracts Neurosciences Abstracts Technology Research Database Engineering Research Database ProQuest Health & Medical Complete (Alumni) Nursing & Allied Health Premium Biotechnology and BioEngineering Abstracts MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Nursing & Allied Health Premium Biotechnology Research Abstracts Technology Research Database ProQuest Health & Medical Complete (Alumni) Engineering Research Database Neurosciences Abstracts Biotechnology and BioEngineering Abstracts MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic Nursing & Allied Health Premium MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Medicine
DocumentTitleAlternate	H. Kabasawa
EISSN	1880-2206
EndPage	82
ExternalDocumentID	PMC9199974 33867419 10_2463_mrms_rev_2021_0011 article_mrms_21_1_21_rev_2021_0011_article_char_en
Genre	Journal Article Review
GroupedDBID	--- .55 2WC 3O- 53G 5GY 7.U ACIWK ACPRK ADBBV AENEX AFRAH ALMA_UNASSIGNED_HOLDINGS AOIJS CS3 DIK F5P GROUPED_DOAJ HYE JSF JSH KQ8 OK1 PGMZT RJT RPM RZJ TKC TR2 X7M AAYXX CITATION CGR CUY CVF ECM EIF NPM 7QO 7TK 8FD FR3 K9. NAPCQ P64 7X8 5PM
ID	FETCH-LOGICAL-c684t-149e899b9441f8c58188fbb951e249b89c2c9a4b4313f6250e80ee85c183ec2e3
ISICitedReferencesCount	36
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000800623300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1347-3182 1880-2206
IngestDate	Tue Sep 30 16:56:36 EDT 2025 Thu Jul 10 22:59:27 EDT 2025 Tue Oct 07 06:35:55 EDT 2025 Thu Jan 02 22:56:38 EST 2025 Sat Nov 29 03:23:13 EST 2025 Tue Nov 18 22:22:15 EST 2025 Wed Sep 03 06:30:43 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	false
IsScholarly	true
Issue	1
Keywords	magnetic resonance imaging image acquisition image reconstruction magnetic resonance imaging system magnetic resonance imaging applications
Language	English
License	https://creativecommons.org/licenses/by-nc-nd/4.0 This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c684t-149e899b9441f8c58188fbb951e249b89c2c9a4b4313f6250e80ee85c183ec2e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23
OpenAccessLink	http://dx.doi.org/10.2463/mrms.rev.2021-0011
PMID	33867419
PQID	2634883723
PQPubID	1976395
PageCount	12
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_9199974 proquest_miscellaneous_2515070931 proquest_journals_2634883723 pubmed_primary_33867419 crossref_primary_10_2463_mrms_rev_2021_0011 crossref_citationtrail_10_2463_mrms_rev_2021_0011 jstage_primary_article_mrms_21_1_21_rev_2021_0011_article_char_en
PublicationCentury	2000
PublicationDate	2022-01-01
PublicationDateYYYYMMDD	2022-01-01
PublicationDate_xml	– month: 01 year: 2022 text: 2022-01-01 day: 01
PublicationDecade	2020
PublicationPlace	Japan
PublicationPlace_xml	– name: Japan – name: Tokyo
PublicationTitle	Magnetic Resonance in Medical Sciences
PublicationTitleAlternate	MRMS
PublicationYear	2022
Publisher	Japanese Society for Magnetic Resonance in Medicine Japan Science and Technology Agency
Publisher_xml	– name: Japanese Society for Magnetic Resonance in Medicine – name: Japan Science and Technology Agency
References	82. Xue H, Inati S, Sørensen TS, Hansen MS, et al. Distributed MRI reconstruction using Gadgetron-based cloud computing. Magn Reson Med 2015; 73:1015–1025. 59. Goto T, Kabasawa H. Robust automated bolus tracker positioning for MRI liver scans. Magn Reson Imaging 2015; 33:63–71. 68. Hori M, Fukunaga I, Masutani Y, et al. Visualizing non-Gaussian diffusion: clinical application of q-space imaging and diffusional kurtosis imaging of the brain and spine. Magn Reson Med Sci 2012; 11:221–233. 32. Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58:1182–1195. 39. Glover GH. Phase-offset multiplanar (POMP) volume imaging: a new technique. J Magn Reson Imaging 1991; 1:457–461. 70. Kabasawa H, Masutani Y, Aoki S, et al. 3T PROPELLER diffusion tensor fiber tractography: a feasibility study for cranial nerve fiber tracking. Radiat Med 2007; 25:462–466. 45. Ichikawa S, Motosugi U, Kromrey ML, et al. Utility of stack-of-stars acquisition for hepatobiliary phase imaging without breath-holding. Magn Reson Med Sci 2020; 19:99–107. 10. Harada T, Kudo K, Uwano I, et al. Breath-holding during the calibration scan improves the reproducibility of parallel transmission at 7T for human brain. Magn Reson Med Sci 2017; 16:23–31. 80. Fujita S, Buonincontri G, Cencini M, et al. Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting. Hum Brain Mapp 2021; 42:275–285. 8. Watanabe H. Investigation of the asymmetric distributions of RF transmission and reception fields at high static field. Magn Reson Med Sci 2012; 11:129–135. 66. Iima M. Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in oncology: applications, challenges, and future trends. Magn Reson Med Sci 2021; 20:125–138. 46. Nakamura Y, Higaki T, Nishihara T, et al. Pseudo-random trajectory scanning suppresses motion artifacts on gadoxetic acid-enhanced hepatobiliary-phase magnetic resonance images. Magn Reson Med Sci 2020; 19:21–28. 81. Fujita S, Hagiwara A, Takei N, et al. Accelerated isotropic multiparametric imaging by high spatial resolution 3D-QALAS with compressed sensing: a phantom, volunteer, and patient study. Invest Radiol 2021; 56:292–300. 73. Kamimura K, Nakajo M, Yoneyama T, et al. Amide proton transfer imaging of tumors: theory, clinical applications, pitfalls, and future directions. Jpn J Radiol 2019; 37:109–116. 78. Kan H, Arai N, Takizawa M, et al. Improvement of signal inhomogeneity induced by radio-frequency transmit-related phase error for single-step quantitative susceptibility mapping reconstruction. Magn Reson Med Sci 2019; 18:276–285. 47. White N, Roddey C, Shankaranarayanan A, et al. PROMO: real-time prospective motion correction in MRI using image-based tracking. Magn Reson Med 2010; 63:91–105. 26. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002; 47:1202–1210. 83. Chelu RG, Wanambiro KW, Hsiao A, et al. Cloud-processed 4D CMR flow imaging for pulmonary flow quantification. Eur J Radiol 2016; 85:1849–1856. 36. Takeshima H, Saitoh K, Nitta S, et al. Estimation of spatiotemporal sensitivity using band-limited signals with no additional acquisitions for k-t parallel imaging. Magn Reson Med Sci 2019; 18:19–28. 35. Yoon JK, Kim MJ, Lee S. Compressed sensing and parallel imaging for double hepatic arterial phase acquisition in gadoxetate-enhanced dynamic liver magnetic resonance imaging. Invest Radiol 2019; 54:374–382. 21. Ichikawa S, Motosugi U, Omori M, et al. MR-guided focused ultrasound for uterine fibroids: a preliminary study of relationship between the treatment outcomes and factors of MR images including elastography. Magn Reson Med Sci 2019; 18:82–87. 53. Kiryu S, Sugano Y, Ohta T, et al. Deep learning-based adaptive noise reduction for improving image quality of 1.5T MR images. Proceedings of the 28th Annual Meeting of ISMRM, On-line, 2020; 3624. 51. 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; 19:195–206. 49. Kyme AZ, Aksoy M, Henry DL, et al. Marker-free optical stereo motion tracking for in-bore MRI and PET-MRI application. Med Phys 2020; 47:3321–3331. 56. Blansit K, Retson T, Masutani E, et al. Deep learning-based prescription of cardiac MRI planes. Radiol Artif Intell 2019; 1:e180069. 69. Maekawa T, Kamiya K, Murata K, et al. Time-dependent diffusion in transient splenial lesion: comparison between oscillating-gradient spin-echo measurements and Monte-Carlo simulation. Magn Reson Med Sci 2021; 20:227–230. 30. Kimura T, Kusahara H. Real-based Polarity-preserving Asymmetric Fourier Imaging (RepAFI). Magn Reson Med Sci 2017; 16:159–168. 61. Miyazaki M, Akahane M. Non-contrast enhanced MR angiography: established techniques. J Magn Reson Imaging 2012; 35:1–19. 74. Kanazawa Y, Fushimi Y, Sakashita N, et al. B1 power optimization for chemical exchange saturation transfer imaging: a phantom study using egg white for amide proton transfer imaging applications in the human brain. Magn Reson Med Sci 2018; 17:86–94. 24. Pruessmann KP, Weiger M, Scheidegger MB, et al. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999; 42:952–962. 54. van Beek EJR, Kuhl C, Anzai Y, et al. Value of MRI in medicine: more than just another test?. J Magn Reson Imaging 2019; 49:e14-e25. 75. Ichikawa S, Motosugi U, Ichikawa T, et al. Magnetic resonance elastography for staging liver fibrosis in chronic hepatitis C. Magn Reson Med Sci 2012; 11:291–297. 71. Mori N, Mugikura S, Miyashita M, et al. Turbo spin-echo diffusion-weighted imaging compared with single-shot echo-planar diffusion-weighted imaging: image quality and diagnostic performance when differentiating between ductal carcinoma in situ and invasive ductal carcinoma. Magn Reson Med Sci 2021; 1:60–68. 65. Irie R, Suzuki M, Yamamoto M, et al. Assessing blood flow in an intracranial stent: a feasibility study of MR angiography using a silent scan after stent-assisted coil embolization for anterior circulation aneurysms. AJNR Am J Neuroradiol 2015; 36:967–970. 60. Miyazaki M, Sugiura S, Tateishi F, et al. Non-contrast-enhanced MR angiography using 3D ECG-synchronized half-Fourier fast spin echo. J Magn Reson Imaging 2000; 12:776–783. 86. Matsuo-Hagiyama C, Watanabe Y, Tanaka H, et al. Comparison of silent and conventional MR imaging for the evaluation of myelination in children. Magn Reson Med Sci 2017; 16:209–216. 63. Shibukawa S, Konta N, Niwa T, et al. Non-enhanced and Non-gated MR angiography for robust visualization of peripheral arteries using enhanced Acceleration-selective Arterial Spin Labeling (eAccASL). Magn Reson Med Sci 2021; 20:312–319. 41. Iwadate Y, Brau AC, Vasanawala SS, et al. Enhancement of respiratory navigator-gated three-dimensional spoiled gradient-recalled echo sequence with variable flip angle scheme. Magn Reson Med 2014; 72:172–177. 9. Bianciardi M, Fukunaga M, van Gelderen P, et al. Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. Magn Reson Imaging 2009; 27:1019–1029. 11. Kameda H, Kudo K, Matsuda T, et al. Improvement of the repeatability of parallel transmission at 7T using interleaved acquisition in the calibration scan. J Magn Reson Imaging 2018; 48:94–101. 29. Mori H, Aoki S, Masumoto T, et al. Two-dimensional magnetic resonance digital subtraction angiography using array spatial sensitivity encoding techniques in the assessment of intracranial hemodynamics. Radiat Med 2002; 20:223–229. 42. Takizawa M, Ito T, Itagaki H, et al. Modified echo peak correction for radial acquisition regime (RADAR). Magn Reson Med Sci 2009; 8:149–158. 17. Gong K, Yang J, Kim K, et al. Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Phys Med Biol 2018; 63:125011. 67. Yamada K, Sakai K, Akazawa K, et al. MR tractography: a review of its clinical applications. Magn Reson Med Sci 2009; 8:165–174. 2. Sasaki M, Inoue T, Tohyama K, et al. High-field MRI of the central nervous system: current approaches to clinical and microscopic imaging. Magn Reson Med Sci 2003; 2:133–139. 18. Zeng F, Nogami M, Ueno YR, et al. Diagnostic performance of zero-TE lung MR imaging in FDG PET/MRI for pulmonary malignancies. Eur Radiol 2020; 30:4995–5003. 76. Yoshimitsu K, Shinagawa Y, Mitsufuji T, et al. Preliminary comparison of multi-scale and multi-model direct inversion algorithms for 3T MR elastography. Magn Reson Med Sci 2017; 16:73–77. 33. Ito S, Arai H, Yamada Y. Compressed sensing in magnetic resonance imaging using the multi-step Fresnel domain band split transformation. Magn Reson Med Sci 2012; 11:243–252. 13. Fujita H. New horizons in MR technology: RF coil designs and trends. Magn Reson Med Sci 2007; 6:29–42. 50. Tamada D, Kromrey ML, Ichikawa S, et al. Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MR imaging of the liver. Magn Reson Med Sci 2020; 19:64–76. 58. Goto T, Kabasawa H. Automated scan prescription for MR imaging of deformed and normal livers. Magn Reson Med Sci 2013; 12:11–20. 25. Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997; 38:591–603. 20. Akram MSH, Obata T, Yamaya T. Microstrip transmission line RF coil for a PET/MRI insert. Magn Reson Med Sci 2020; 19:147–153. 52. Nakajima S, Fushimi Y, Yokota Y, et al. Application of deep learning reconstruction to compressed-sensing thin-slice fat-suppressed T2-weighted imaging of the orbit. Proceedings of the 28th Annual Meeting of ISMRM, On-line, 2020; 2024. 77. Sato R, Shirai T, Taniguchi Y, et al. Quantitative susceptibility mapping using the multiple dipole-inversion combination with k-space segmentation method. Magn Reson Med Sci 2017; 16:340–350. 85. Kodama N, Setoi A, Kose K. Spiral MRI on a 9.4T vertical-bore 44 45 46 47 48 49 50 51 52 53 10 54 11 55 12 56 13 57 14 58 15 59 16 17 18 19 1 2 3 4 5 6 7 8 9 60 61 62 63 20 64 21 65 22 66 23 67 24 68 25 69 26 27 28 29 70 71 72 73 30 74 31 75 32 76 33 77 34 78 35 79 36 37 38 39 80 81 82 83 40 84 41 85 42 86 43 87
References_xml	– reference: 85. Kodama N, Setoi A, Kose K. Spiral MRI on a 9.4T vertical-bore superconducting magnet using unshielded and self-shielded gradient coils. Magn Reson Med Sci 2018; 17:174–183. – reference: 1. Fukatsu H. 3T MR for clinical use: update. Magn Reson Med Sci 2003; 2:37–45. – reference: 33. Ito S, Arai H, Yamada Y. Compressed sensing in magnetic resonance imaging using the multi-step Fresnel domain band split transformation. Magn Reson Med Sci 2012; 11:243–252. – reference: 50. Tamada D, Kromrey ML, Ichikawa S, et al. Motion artifact reduction using a convolutional neural network for dynamic contrast enhanced MR imaging of the liver. Magn Reson Med Sci 2020; 19:64–76. – reference: 58. Goto T, Kabasawa H. Automated scan prescription for MR imaging of deformed and normal livers. Magn Reson Med Sci 2013; 12:11–20. – reference: 40. Setsompop K, Gagoski BA, Polimeni JR, et al. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med 2012; 67:1210–1224. – reference: 62. Takei N, Miyoshi M, Kabasawa H. Noncontrast MR angiography for supraaortic arteries using inflow enhanced inversion recovery fast spin echo imaging. J Magn Reson Imaging 2012; 35:957–962. – reference: 57. Sreekumari A, Shanbhag D, Yeo D, et al. A deep learning-based approach to reduce rescan and recall rates in clinical MRI examinations. AJNR Am J Neuroradiol 2019; 40:217–223. – reference: 78. Kan H, Arai N, Takizawa M, et al. Improvement of signal inhomogeneity induced by radio-frequency transmit-related phase error for single-step quantitative susceptibility mapping reconstruction. Magn Reson Med Sci 2019; 18:276–285. – reference: 65. Irie R, Suzuki M, Yamamoto M, et al. Assessing blood flow in an intracranial stent: a feasibility study of MR angiography using a silent scan after stent-assisted coil embolization for anterior circulation aneurysms. AJNR Am J Neuroradiol 2015; 36:967–970. – reference: 43. Pipe JG. Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn Reson Med 1999; 42:963–969. – reference: 79. Ikebe Y, Ishimaru H, Imai H, et al. Quantitative susceptibility mapping for carotid atherosclerotic plaques: a pilot study. Magn Reson Med Sci 2020; 19:135–140. – reference: 32. Lustig M, Donoho D, Pauly JM. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn Reson Med 2007; 58:1182–1195. – reference: 7. Uwano I, Metoki T, Sendai F, et al. Assessment of sensations experienced by subjects during MR imaging examination at 7T. Magn Reson Med Sci 2015; 14:35–41. – reference: 75. Ichikawa S, Motosugi U, Ichikawa T, et al. Magnetic resonance elastography for staging liver fibrosis in chronic hepatitis C. Magn Reson Med Sci 2012; 11:291–297. – reference: 13. Fujita H. New horizons in MR technology: RF coil designs and trends. Magn Reson Med Sci 2007; 6:29–42. – reference: 28. Takizawa M, Shimoda T, Nonaka M, et al. Parallel imaging of head with a dedicated multi-coil on a 0.4T open MRI. Magn Reson Med Sci 2005; 4:95–101. – reference: 17. Gong K, Yang J, Kim K, et al. Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images. Phys Med Biol 2018; 63:125011. – reference: 36. Takeshima H, Saitoh K, Nitta S, et al. Estimation of spatiotemporal sensitivity using band-limited signals with no additional acquisitions for k-t parallel imaging. Magn Reson Med Sci 2019; 18:19–28. – reference: 53. Kiryu S, Sugano Y, Ohta T, et al. Deep learning-based adaptive noise reduction for improving image quality of 1.5T MR images. Proceedings of the 28th Annual Meeting of ISMRM, On-line, 2020; 3624. – reference: 21. Ichikawa S, Motosugi U, Omori M, et al. MR-guided focused ultrasound for uterine fibroids: a preliminary study of relationship between the treatment outcomes and factors of MR images including elastography. Magn Reson Med Sci 2019; 18:82–87. – reference: 26. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002; 47:1202–1210. – reference: 20. Akram MSH, Obata T, Yamaya T. Microstrip transmission line RF coil for a PET/MRI insert. Magn Reson Med Sci 2020; 19:147–153. – reference: 5. Kataoka M, Kido A, Koyama T, et al. MRI of the female pelvis at 3T compared to 1.5T: evaluation on high-resolution T2-weighted and HASTE images. J Magn Reson Imaging 2007; 25:527–534. – reference: 51. 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; 19:195–206. – reference: 67. Yamada K, Sakai K, Akazawa K, et al. MR tractography: a review of its clinical applications. Magn Reson Med Sci 2009; 8:165–174. – reference: 81. Fujita S, Hagiwara A, Takei N, et al. Accelerated isotropic multiparametric imaging by high spatial resolution 3D-QALAS with compressed sensing: a phantom, volunteer, and patient study. Invest Radiol 2021; 56:292–300. – reference: 44. Chandarana H, Feng L, Block TK, et al. Free-breathing contrast-enhanced multiphase MRI of the liver using a combination of compressed sensing, parallel imaging, and golden-angle radial sampling. Invest Radiol 2013; 48:10–16. – reference: 55. Yokoyama K, Ishimura R, Kariyasu T, et al. Clinical application of an automatic slice-alignment method for cardiac MR imaging. Magn Reson Med Sci 2014; 13:293–298. – reference: 27. Takizawa M, Goto T, Mochizuki H, et al. Cardiac cine parallel imaging on a 0.7T open system. Magn Reson Med Sci 2004; 3:45–49. – reference: 8. Watanabe H. Investigation of the asymmetric distributions of RF transmission and reception fields at high static field. Magn Reson Med Sci 2012; 11:129–135. – reference: 82. Xue H, Inati S, Sørensen TS, Hansen MS, et al. Distributed MRI reconstruction using Gadgetron-based cloud computing. Magn Reson Med 2015; 73:1015–1025. – reference: 30. Kimura T, Kusahara H. Real-based Polarity-preserving Asymmetric Fourier Imaging (RepAFI). Magn Reson Med Sci 2017; 16:159–168. – reference: 59. Goto T, Kabasawa H. Robust automated bolus tracker positioning for MRI liver scans. Magn Reson Imaging 2015; 33:63–71. – reference: 66. Iima M. Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in oncology: applications, challenges, and future trends. Magn Reson Med Sci 2021; 20:125–138. – reference: 9. Bianciardi M, Fukunaga M, van Gelderen P, et al. Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. Magn Reson Imaging 2009; 27:1019–1029. – reference: 76. Yoshimitsu K, Shinagawa Y, Mitsufuji T, et al. Preliminary comparison of multi-scale and multi-model direct inversion algorithms for 3T MR elastography. Magn Reson Med Sci 2017; 16:73–77. – reference: 2. Sasaki M, Inoue T, Tohyama K, et al. High-field MRI of the central nervous system: current approaches to clinical and microscopic imaging. Magn Reson Med Sci 2003; 2:133–139. – reference: 86. Matsuo-Hagiyama C, Watanabe Y, Tanaka H, et al. Comparison of silent and conventional MR imaging for the evaluation of myelination in children. Magn Reson Med Sci 2017; 16:209–216. – reference: 69. Maekawa T, Kamiya K, Murata K, et al. Time-dependent diffusion in transient splenial lesion: comparison between oscillating-gradient spin-echo measurements and Monte-Carlo simulation. Magn Reson Med Sci 2021; 20:227–230. – reference: 70. Kabasawa H, Masutani Y, Aoki S, et al. 3T PROPELLER diffusion tensor fiber tractography: a feasibility study for cranial nerve fiber tracking. Radiat Med 2007; 25:462–466. – reference: 25. Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997; 38:591–603. – reference: 72. Kishimoto AO, Kataoka M, Iima M, et al. Evaluation of malignant breast lesions using high-resolution readout-segmented diffusion-weighted echo-planar imaging: comparison with pathology. Magn Reson Med Sci 2021; 20:204–215. – reference: 73. Kamimura K, Nakajo M, Yoneyama T, et al. Amide proton transfer imaging of tumors: theory, clinical applications, pitfalls, and future directions. Jpn J Radiol 2019; 37:109–116. – reference: 80. Fujita S, Buonincontri G, Cencini M, et al. Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting. Hum Brain Mapp 2021; 42:275–285. – reference: 49. Kyme AZ, Aksoy M, Henry DL, et al. Marker-free optical stereo motion tracking for in-bore MRI and PET-MRI application. Med Phys 2020; 47:3321–3331. – reference: 39. Glover GH. Phase-offset multiplanar (POMP) volume imaging: a new technique. J Magn Reson Imaging 1991; 1:457–461. – reference: 61. Miyazaki M, Akahane M. Non-contrast enhanced MR angiography: established techniques. J Magn Reson Imaging 2012; 35:1–19. – reference: 84. Dietrich BE, Brunner DO, Wilm BJ, et al. A field camera for MR sequence monitoring and system analysis. Magn Reson Med 2016; 75:1831–1840. – reference: 3. Naganawa S, Kawai H, Fukatsu H, et al. High-speed imaging at 3 Tesla: a technical and clinical review with an emphasis on whole-brain 3D imaging. Magn Reson Med Sci 2004; 3:177–187. – reference: 16. Wiesinger F, Bylund M, Yang J, et al. Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning. Magn Reson Med 2018; 80:1440–1451. – reference: 11. Kameda H, Kudo K, Matsuda T, et al. Improvement of the repeatability of parallel transmission at 7T using interleaved acquisition in the calibration scan. J Magn Reson Imaging 2018; 48:94–101. – reference: 38. Sekine T, Amano Y, Takagi R, et al. Feasibility of 4D flow MR imaging of the brain with either Cartesian y-z radial sampling or k-t SENSE: comparison with 4D Flow MR imaging using SENSE. Magn Reson Med Sci 2014; 13:15–24. – reference: 63. Shibukawa S, Konta N, Niwa T, et al. Non-enhanced and Non-gated MR angiography for robust visualization of peripheral arteries using enhanced Acceleration-selective Arterial Spin Labeling (eAccASL). Magn Reson Med Sci 2021; 20:312–319. – reference: 23. Kuroda K. MR techniques for guiding high-intensity focused ultrasound (HIFU) treatments. J Magn Reson Imaging 2018; 47:316–331. – reference: 29. Mori H, Aoki S, Masumoto T, et al. Two-dimensional magnetic resonance digital subtraction angiography using array spatial sensitivity encoding techniques in the assessment of intracranial hemodynamics. Radiat Med 2002; 20:223–229. – reference: 54. van Beek EJR, Kuhl C, Anzai Y, et al. Value of MRI in medicine: more than just another test?. J Magn Reson Imaging 2019; 49:e14-e25. – reference: 15. Ishii S, Hara T, Nanbu T, et al. Optimized workflow and imaging protocols for whole-body oncologic PET/MRI. Jpn J Radiol 2016; 34:754–762. – reference: 10. Harada T, Kudo K, Uwano I, et al. Breath-holding during the calibration scan improves the reproducibility of parallel transmission at 7T for human brain. Magn Reson Med Sci 2017; 16:23–31. – reference: 71. Mori N, Mugikura S, Miyashita M, et al. Turbo spin-echo diffusion-weighted imaging compared with single-shot echo-planar diffusion-weighted imaging: image quality and diagnostic performance when differentiating between ductal carcinoma in situ and invasive ductal carcinoma. Magn Reson Med Sci 2021; 1:60–68. – reference: 12. Roemer PB, Edelstein WA, Hayes CE, et al. The NMR phased array. Magn Reson Med 1990; 16:192–225. – reference: 56. Blansit K, Retson T, Masutani E, et al. Deep learning-based prescription of cardiac MRI planes. Radiol Artif Intell 2019; 1:e180069. – reference: 74. Kanazawa Y, Fushimi Y, Sakashita N, et al. B1 power optimization for chemical exchange saturation transfer imaging: a phantom study using egg white for amide proton transfer imaging applications in the human brain. Magn Reson Med Sci 2018; 17:86–94. – reference: 60. Miyazaki M, Sugiura S, Tateishi F, et al. Non-contrast-enhanced MR angiography using 3D ECG-synchronized half-Fourier fast spin echo. J Magn Reson Imaging 2000; 12:776–783. – reference: 64. Suzuki Y, Fujima N, van Osch MJP. Intracranial 3D and 4D MR angiography using arterial spin labeling: technical considerations. Magn Reson Med Sci 2020; 19:294–309. – reference: 45. Ichikawa S, Motosugi U, Kromrey ML, et al. Utility of stack-of-stars acquisition for hepatobiliary phase imaging without breath-holding. Magn Reson Med Sci 2020; 19:99–107. – reference: 52. Nakajima S, Fushimi Y, Yokota Y, et al. Application of deep learning reconstruction to compressed-sensing thin-slice fat-suppressed T2-weighted imaging of the orbit. Proceedings of the 28th Annual Meeting of ISMRM, On-line, 2020; 2024. – reference: 87. Iwadate Y, Nozaki A, Nunokawa Y, et al. Comparison of silent navigator waveform generation methods. Magn Reson Med Sci 2020; 19:154–158. – reference: 37. Okuda S, Yamada Y, Tanimoto A, et al. Three-dimensional cardiac cine imaging using the kat ARC acceleration: Initial experience in clinical adult patients at 3T. Magn Reson Imaging 2015; 33:911–917. – reference: 18. Zeng F, Nogami M, Ueno YR, et al. Diagnostic performance of zero-TE lung MR imaging in FDG PET/MRI for pulmonary malignancies. Eur Radiol 2020; 30:4995–5003. – reference: 35. Yoon JK, Kim MJ, Lee S. Compressed sensing and parallel imaging for double hepatic arterial phase acquisition in gadoxetate-enhanced dynamic liver magnetic resonance imaging. Invest Radiol 2019; 54:374–382. – reference: 41. Iwadate Y, Brau AC, Vasanawala SS, et al. Enhancement of respiratory navigator-gated three-dimensional spoiled gradient-recalled echo sequence with variable flip angle scheme. Magn Reson Med 2014; 72:172–177. – reference: 31. Kimura T, Shigeta T. Magnitude-based asymmetric fourier imaging (MagAFI). Magn Reson Med Sci 2016; 15:94–104. – reference: 48. Watanabe K, Kakeda S, Igata N, et al. Utility of real-time prospective motion correction (PROMO) on 3D T1-weighted imaging in automated brain structure measurements. Sci Rep 2016; 6:38366. – reference: 6. Nakada T, Nabetani A, Kabasawa H, et al. The passage to human MR microscopy: a progress report from Niigata on April 2005. Magn Reson Med Sci 2005; 4:83–87. – reference: 34. Kromrey ML, Funayama S, Tamada D, et al. Clinical evaluation of respiratory-triggered 3D MRCP with navigator echoes compared to breath-hold acquisition using compressed sensing and/or parallel imaging. Magn Reson Med Sci 2020; 19:318–323. – reference: 83. Chelu RG, Wanambiro KW, Hsiao A, et al. Cloud-processed 4D CMR flow imaging for pulmonary flow quantification. Eur J Radiol 2016; 85:1849–1856. – reference: 68. Hori M, Fukunaga I, Masutani Y, et al. Visualizing non-Gaussian diffusion: clinical application of q-space imaging and diffusional kurtosis imaging of the brain and spine. Magn Reson Med Sci 2012; 11:221–233. – reference: 46. Nakamura Y, Higaki T, Nishihara T, et al. Pseudo-random trajectory scanning suppresses motion artifacts on gadoxetic acid-enhanced hepatobiliary-phase magnetic resonance images. Magn Reson Med Sci 2020; 19:21–28. – reference: 24. Pruessmann KP, Weiger M, Scheidegger MB, et al. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999; 42:952–962. – reference: 22. Elias WJ, Lipsman N, Ondo WG, et al. A randomized trial of focused ultrasound thalamotomy for essential tremor. N Engl J Med 2016; 375:730–739. – reference: 42. Takizawa M, Ito T, Itagaki H, et al. Modified echo peak correction for radial acquisition regime (RADAR). Magn Reson Med Sci 2009; 8:149–158. – reference: 47. White N, Roddey C, Shankaranarayanan A, et al. PROMO: real-time prospective motion correction in MRI using image-based tracking. Magn Reson Med 2010; 63:91–105. – reference: 77. Sato R, Shirai T, Taniguchi Y, et al. Quantitative susceptibility mapping using the multiple dipole-inversion combination with k-space segmentation method. Magn Reson Med Sci 2017; 16:340–350. – reference: 4. Uematsu H, Takahashi M, Dougherty L, et al. High field body MR imaging: preliminary experiences. Clin Imaging 2004; 28:159–162. – reference: 19. Okazawa H, Higashino Y, Tsujikawa T, et al. Noninvasive method for measurement of cerebral blood flow using O-15 water PET/MRI with ASL correlation. Eur J Radiol 2018; 105:102–109. – reference: 14. McGee KP, Stormont RS, Lindsay SA, et al. Characterization and evaluation of a flexible MRI receive coil array for radiation therapy MR treatment planning using highly decoupled RF circuits. Phys Med Biol 2018; 63:08NT02. – ident: 28 doi: 10.2463/mrms.4.95 – ident: 57 doi: 10.3174/ajnr.A5926 – ident: 70 doi: 10.1007/s11604-007-0169-8 – ident: 85 doi: 10.2463/mrms.tn.2016-0049 – ident: 62 doi: 10.1002/jmri.23515 – ident: 5 doi: 10.1002/jmri.20842 – ident: 18 doi: 10.1007/s00330-020-06848-z – ident: 59 doi: 10.1016/j.mri.2014.08.038 – ident: 17 doi: 10.1088/1361-6560/aac763 – ident: 64 doi: 10.2463/mrms.rev.2019-0096 – ident: 79 doi: 10.2463/mrms.mp.2018-0077 – ident: 42 doi: 10.2463/mrms.8.149 – ident: 20 doi: 10.2463/mrms.mp.2019-0137 – ident: 63 doi: 10.2463/mrms.tn.2019-0172 – ident: 21 doi: 10.2463/mrms.tn.2017-0103 – ident: 4 doi: 10.1016/S0899-7071(03)00151-7 – ident: 33 doi: 10.2463/mrms.11.243 – ident: 6 doi: 10.2463/mrms.4.83 – ident: 82 doi: 10.1002/mrm.25213 – ident: 7 doi: 10.2463/mrms.2014-0004 – ident: 16 doi: 10.1002/mrm.27134 – ident: 12 doi: 10.1002/mrm.1910160203 – ident: 34 doi: 10.2463/mrms.mp-2019-0122 – ident: 49 doi: 10.1002/mp.14199 – ident: 65 doi: 10.3174/ajnr.A4199 – ident: 69 doi: 10.2463/mrms.bc.2020-0046 – ident: 83 doi: 10.1016/j.ejrad.2016.07.018 – ident: 30 doi: 10.2463/mrms.tn.2015-0152 – ident: 61 doi: 10.1002/jmri.22789 – ident: 22 doi: 10.1056/NEJMoa1600159 – ident: 48 doi: 10.1038/srep38366 – ident: 51 doi: 10.2463/mrms.mp.2019-0018 – ident: 60 doi: 10.1002/1522-2586(200011)12:5<776::AID-JMRI17>3.0.CO;2-X – ident: 27 doi: 10.2463/mrms.3.45 – ident: 67 doi: 10.2463/mrms.8.165 – ident: 19 doi: 10.1016/j.ejrad.2018.05.033 – ident: 26 doi: 10.1002/mrm.10171 – ident: 81 doi: 10.1097/RLI.0000000000000744 – ident: 54 doi: 10.1002/jmri.26211 – ident: 76 doi: 10.2463/mrms.mp.2016-0047 – ident: 45 doi: 10.2463/mrms.mp.2019-0030 – ident: 71 doi: 10.2463/mrms.mp.2019-0195 – ident: 2 doi: 10.2463/mrms.2.133 – ident: 56 doi: 10.1148/ryai.2019180069 – ident: 1 doi: 10.2463/mrms.2.37 – ident: 75 doi: 10.2463/mrms.11.291 – ident: 3 doi: 10.2463/mrms.3.177 – ident: 23 doi: 10.1002/jmri.25770 – ident: 58 doi: 10.2463/mrms.2012-0006 – ident: 80 doi: 10.1002/hbm.25232 – ident: 66 doi: 10.2463/mrms.rev.2019-0124 – ident: 78 doi: 10.2463/mrms.tn.2018-0066 – ident: 15 doi: 10.1007/s11604-016-0584-9 – ident: 38 doi: 10.2463/mrms.2013-0008 – ident: 53 – ident: 14 doi: 10.1088/1361-6560/aab691 – ident: 9 doi: 10.1016/j.mri.2009.02.004 – ident: 36 doi: 10.2463/mrms.mp.2017-0132 – ident: 8 doi: 10.2463/mrms.11.129 – ident: 39 doi: 10.1002/jmri.1880010410 – ident: 50 doi: 10.2463/mrms.mp.2018-0156 – ident: 11 doi: 10.1002/jmri.25903 – ident: 84 doi: 10.1002/mrm.25770 – ident: 31 doi: 10.2463/mrms.2014-0147 – ident: 37 doi: 10.1016/j.mri.2015.04.004 – ident: 35 doi: 10.1097/RLI.0000000000000548 – ident: 41 doi: 10.1002/mrm.24902 – ident: 86 doi: 10.2463/mrms.mp.2016-0045 – ident: 24 doi: 10.1002/(SICI)1522-2594(199911)42:5<952::AID-MRM16>3.0.CO;2-S – ident: 55 doi: 10.2463/mrms.2013-0127 – ident: 10 doi: 10.2463/mrms.mp.2015-0137 – ident: 46 doi: 10.2463/mrms.mp.2018-0174 – ident: 87 doi: 10.2463/mrms.tn.2018-0168 – ident: 25 doi: 10.1002/mrm.1910380414 – ident: 44 doi: 10.1097/RLI.0b013e318271869c – ident: 52 – ident: 73 doi: 10.1007/s11604-018-0787-3 – ident: 72 doi: 10.2463/mrms.mp.2020-0021 – ident: 29 – ident: 77 doi: 10.2463/mrms.mp.2016-0062 – ident: 13 doi: 10.2463/mrms.6.29 – ident: 74 doi: 10.2463/mrms.tn.2016-0069 – ident: 68 doi: 10.2463/mrms.11.221 – ident: 32 doi: 10.1002/mrm.21391 – ident: 47 doi: 10.1002/mrm.22176 – ident: 40 doi: 10.1002/mrm.23097 – ident: 43 doi: 10.1002/(SICI)1522-2594(199911)42:5<963::AID-MRM17>3.0.CO;2-L
SSID	ssj0038188
Score	2.4184806
SecondaryResourceType	review_article
Snippet	Clinical MRI systems have continually improved over the years since their introduction in the 1980s. In MRI technical development, the developments in each MRI...
SourceID	pubmedcentral proquest pubmed crossref jstage
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	71
SubjectTerms	Data acquisition Data processing Electronics industry Hybrid systems Image acquisition Image processing Image Processing, Computer-Assisted - methods Image reconstruction Innovations Inventions Magnetic resonance imaging Magnetic Resonance Imaging - methods magnetic resonance imaging applications magnetic resonance imaging system Medical imaging New technology Positron emission Positron emission tomography Review Tomography
Title	MR Imaging in the 21st Century: Technical Innovation over the First Two Decades
URI	https://www.jstage.jst.go.jp/article/mrms/21/1/21_rev.2021-0011/_article/-char/en https://www.ncbi.nlm.nih.gov/pubmed/33867419 https://www.proquest.com/docview/2634883723 https://www.proquest.com/docview/2515070931 https://pubmed.ncbi.nlm.nih.gov/PMC9199974
Volume	21
WOSCitedRecordID	wos000800623300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
ispartofPNX	Magnetic Resonance in Medical Sciences, 2022, Vol.21(1), pp.71-82
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1880-2206 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038188 issn: 1347-3182 databaseCode: DOA dateStart: 20160101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELfYmBAviG8CYzIS4qUKJHY-bN4mtGqT1g5NmdS3KEmdLUDT0rRs_Pfc2U7abhMaD7xYlXOpo7vz-Xf2-Y6Q914WRtmY-64KS-UG-Vi5chxxF9bWMi9yViqV62IT8XAoRiP51VbpbHQ5gbiuxdWVnP1XUUMfCBuvzv6DuLs_hQ74DUKHFsQO7Z0EPzjtHU1M7SEbw8j8ZqG3cfHMvN1O18I56mqi9jCUU1P3KwCEveRyCrYIg-ebdfw6yM5rZdI-I4ZHqwCDTOxpj11OV-dDGSyS2aXGp4fVfPp7-b1a32ZgbG2bwVhGmOguY57NW31LnzWnzL-hNsY2mlIr1002CyJMHTGZT5qPWHWHYcwIAtXVAtUeyg9P0v7Z8XGaHIySD7OfLpYOwyN2W0dli9xncShF61eb5RgRib4P2X6ruTmFw366OegGOtn5BgD9XN3me1wPoV3DJMlj8sg6E3TfKMETck_VT8mDgQ2XeEZOBqfU6gKtagrSpagL1OrCZ9ppAl1pAkVN0LRaEyhoArWa8Jyc9Q-SL4euraDhFpEIFi64vwoc6lwC6C1FESIvyjwHVK3A7c6FLFghsyAHFMlL8IQ9JTylRFiAoVcFU_wF2a6ntXpFKDDMYzEbAwLnwVh6mSdidK45k-ADBMwhfsu5tLDp5bHKyY8U3EzkdorcToHbKXJbx1I6pNe9MzPJVf5KvW8E0tHaOWZogcrHZuOdjgTvMIJ1cMhuK8vUzll4M-KwjPGYcYe86x6DmcWzs6xW0yXQhOg5eZLDZ7w0ou8-g3MRATCXDok3lKIjwBTum0_q6kKncpeYBSQOXt9h3Dfk4Wpe7pLtxXyp3pKd4teiauZ7ZCseiT2t-X8A6su8pg
linkProvider	Directory of Open Access Journals
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=MR+Imaging+in+the+21st+Century%3A+Technical+Innovation+over+the+First+Two+Decades&rft.jtitle=Magnetic+resonance+in+medical+sciences&rft.au=Kabasawa%2C+Hiroyuki&rft.date=2022-01-01&rft.issn=1880-2206&rft.eissn=1880-2206&rft.volume=21&rft.issue=1&rft.spage=71&rft_id=info:doi/10.2463%2Fmrms.rev.2021-0011&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1347-3182&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1347-3182&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1347-3182&client=summon