Robitaille PM, Abduljalil AM, Kangarlu A et al (1998) Human magnetic resonance imaging at 8 T. NMR Biomed 11:263–265 https://doi.org/10.1002/(sici)1099-1492(199810)11:6<263::aid-nbm549>3.0.co;2-0
Article
CAS
PubMed
Google Scholar
Budinger TF, Bird MD (2018) MRI and MRS of the human brain at magnetic fields of 14T to 20T: Technical feasibility, safety, and neuroscience horizons. Neuroimage. 168:509–531 https://doi.org/10.1016/j.neuroimage.2017.01.067
Article
PubMed
Google Scholar
Pohmann R, Speck O, Scheffler K (2016) Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 tesla using current receive coil arrays. Magn Reson Med 75:801–809 https://doi.org/10.1002/mrm.25677
Article
PubMed
Google Scholar
Tian Q, Bilgic B, Fan Q et al (2020) Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution. Cereb Cortex https://doi.org/10.1093/cercor/bhaa237
Zaretskaya N, Fischl B, Reuter M, Renvall V, Polimeni JR (2018) Advantages of cortical surface reconstruction using submillimeter 7 T MEMPRAGE. Neuroimage. 165:11–26 https://doi.org/10.1016/j.neuroimage.2017.09.060
Article
PubMed
Google Scholar
Wisse LE, Kuijf HJ, Honingh AM et al (2016) Automated hippocampal subfield segmentation at 7T MRI. AJNR Am J Neuroradiol 37:1050–1057 https://doi.org/10.3174/ajnr.A4659
Article
CAS
PubMed
PubMed Central
Google Scholar
Iglesias JE, Augustinack JC, Nguyen K et al (2015) A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. Neuroimage. 115:117–137 https://doi.org/10.1016/j.neuroimage.2015.04.042
Article
PubMed
Google Scholar
Solano-Castiella E, Schafer A, Reimer E et al (2011) Parcellation of human amygdala in vivo using ultra high field structural MRI. Neuroimage. 58:741–748 https://doi.org/10.1016/j.neuroimage.2011.06.047
Article
PubMed
Google Scholar
Saygin ZM, Kliemann D, Iglesias JE et al (2017) High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage. 155:370–382 https://doi.org/10.1016/j.neuroimage.2017.04.046
Article
CAS
PubMed
Google Scholar
von Morze C, Xu D, Purcell DD et al (2007) Intracranial time-of-flight MR angiography at 7T with comparison to 3T. J Magn Reson Imaging 26:900–904 https://doi.org/10.1002/jmri.21097
Article
Google Scholar
Gardener AG, Gowland PA, Francis ST (2009) Implementation of quantitative perfusion imaging using pulsed arterial spin labeling at ultra-high field. Magn Reson Med 61:874–882 https://doi.org/10.1002/mrm.21796
Article
CAS
PubMed
Google Scholar
Gati JS, Menon RS, Ugurbil K, Rutt BK (1997) Experimental determination of the BOLD field strength dependence in vessels and tissue. Magn Reson Med 38:296–302 https://doi.org/10.1002/mrm.1910380220
Article
CAS
PubMed
Google Scholar
Triantafyllou C, Hoge RD, Krueger G et al (2005) Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters. Neuroimage. 26:243–250 https://doi.org/10.1016/j.neuroimage.2005.01.007
Article
CAS
PubMed
Google Scholar
Hoff MN, McKinney A, Shellock FG et al (2019) Safety Considerations of 7-T MRI in Clinical Practice. Radiology. 292:509–518 https://doi.org/10.1148/radiol.2019182742
Article
PubMed
Google Scholar
Shellock FG (2020) Reference manual for magnetic resonance safety, implants, and devices. Biomedical Research Publishing Group, California
Google Scholar
Dula AN, Virostko J, Shellock FG (2014) Assessment of MRI issues at 7 T for 28 implants and other objects. AJR Am J Roentgenol 202:401–405 https://doi.org/10.2214/AJR.13.10777
Article
PubMed
Google Scholar
Fagan AJ, Bitz AK, Bjorkman-Burtscher IM et al (2021) 7T MR Safety. J Magn Reson Imaging 53:333–346 https://doi.org/10.1002/jmri.27319
Article
PubMed
Google Scholar
Caballero-Gaudes C, Reynolds RC (2017) Methods for cleaning the BOLD fMRI signal. Neuroimage. 154:128–149 https://doi.org/10.1016/j.neuroimage.2016.12.018
Article
PubMed
Google Scholar
Hutton C, Josephs O, Stadler J et al (2011) The impact of physiological noise correction on fMRI at 7 T. Neuroimage. 57:101–112 https://doi.org/10.1016/j.neuroimage.2011.04.018
Article
CAS
PubMed
Google Scholar
Nowell M, Miserocchi A, McEvoy AW, Duncan JS (2014) Advances in epilepsy surgery. J Neurol Neurosurg Psychiatry 85:1273–1279 https://doi.org/10.1136/jnnp-2013-307069
Article
PubMed
Google Scholar
Opheim G, van der Kolk A, Bloch KM et al (2020) 7T Epilepsy Task Force Consensus Recommendations on the use of 7T in Clinical Practice. Neurology. https://doi.org/10.1212/WNL.0000000000011413
Obusez EC, Lowe M, Oh SH et al (2018) 7T MR of intracranial pathology: preliminary observations and comparisons to 3T and 1.5T. Neuroimage. 168:459–476 https://doi.org/10.1016/j.neuroimage.2016.11.030
Article
PubMed
Google Scholar
Verma G, Delman BN, Balchandani P (2021) UltraHigh Field MR Imaging in Epilepsy. Magn Reson Imaging Clin N Am 29:41–52 https://doi.org/10.1016/j.mric.2020.09.006
Article
PubMed
PubMed Central
Google Scholar
Feldman RE, Delman BN, Pawha PS et al (2019) 7T MRI in epilepsy patients with previously normal clinical MRI exams compared against healthy controls. PLoS One 14:e0213642 https://doi.org/10.1371/journal.pone.0213642
Article
CAS
PubMed
PubMed Central
Google Scholar
Schlamann M, Maderwald S, Becker W et al (2010) Cerebral cavernous hemangiomas at 7 Tesla: initial experience. Acad Radiol 17:3–6 https://doi.org/10.1016/j.acra.2009.10.001
Article
PubMed
Google Scholar
Wang I, Oh S, Blumcke I et al (2020) Value of 7T MRI and post-processing in patients with nonlesional 3T MRI undergoing epilepsy presurgical evaluation. Epilepsia. 61:2509–2520 https://doi.org/10.1111/epi.16682
Article
PubMed
PubMed Central
Google Scholar
Feldman RE, Rutland JW, Fields MC et al (2018) Quantification of perivascular spaces at 7T: A potential MRI biomarker for epilepsy. Seizure. 54:11–18 https://doi.org/10.1016/j.seizure.2017.11.004
Article
PubMed
Google Scholar
Stefanits H, Springer E, Pataraia E et al (2017) Seven-tesla MRI of hippocampal sclerosis: an in vivo feasibility study with histological correlations. Investig Radiol 52:666–671 https://doi.org/10.1097/RLI.0000000000000388
Article
Google Scholar
Feldman RE, Marcuse LV, Verma G et al (2020) Seven-tesla susceptibility-weighted analysis of hippocampal venous structures: application to magnetic-resonance-normal focal epilepsy. Epilepsia. 61:287–296 https://doi.org/10.1111/epi.16433
Article
PubMed
PubMed Central
Google Scholar
Voets NL, Hodgetts CJ, Sen A, Adcock JE, Emir U (2017) Hippocampal MRS and subfield volumetry at 7T detects dysfunction not specific to seizure focus. Sci Rep 7:16138 https://doi.org/10.1038/s41598-017-16046-5
Article
PubMed
PubMed Central
CAS
Google Scholar
Shah P, Bassett DS, Wisse LEM et al (2019) Structural and functional asymmetry of medial temporal subregions in unilateral temporal lobe epilepsy: A 7T MRI study. Hum Brain Mapp 40:2390–2398 https://doi.org/10.1002/hbm.24530
Article
PubMed
PubMed Central
Google Scholar
Bruschi N, Boffa G, Inglese M (2020) Ultra-high-field 7-T MRI in multiple sclerosis and other demyelinating diseases: from pathology to clinical practice. Eur Radiol Exp 4:59 https://doi.org/10.1186/s41747-020-00186-x
Article
PubMed
PubMed Central
Google Scholar
Sati P, Oh J, Constable RT et al (2016) The central vein sign and its clinical evaluation for the diagnosis of multiple sclerosis: a consensus statement from the North American Imaging in Multiple Sclerosis Cooperative. Nat Rev Neurol 12:714–722 https://doi.org/10.1038/nrneurol.2016.166
Article
PubMed
Google Scholar
Geraldes R, Ciccarelli O, Barkhof F et al (2018) The current role of MRI in differentiating multiple sclerosis from its imaging mimics. Nat Rev Neurol 14:213 https://doi.org/10.1038/nrneurol.2018.39
Article
PubMed
Google Scholar
Tallantyre EC, Morgan PS, Dixon JE et al (2009) A comparison of 3T and 7T in the detection of small parenchymal veins within MS lesions. Investig Radiol 44:491–494 https://doi.org/10.1097/RLI.0b013e3181b4c144
Article
Google Scholar
Tallantyre EC, Dixon JE, Donaldson I et al (2011) Ultra-high-field imaging distinguishes MS lesions from asymptomatic white matter lesions. Neurology. 76:534–539 https://doi.org/10.1212/WNL.0b013e31820b7630
Article
CAS
PubMed
PubMed Central
Google Scholar
Hosseini Z, Matusinec J, Rudko DA et al (2018) Morphology-specific discrimination between MS white matter lesions and benign white matter hyperintensities using ultra-high-field MRI. AJNR Am J Neuroradiol 39:1473–1479 https://doi.org/10.3174/ajnr.A5705
CAS
PubMed
PubMed Central
Google Scholar
Castellaro M, Tamanti A, Pisani AI, Pizzini FB, Crescenzo F, Calabrese M (2020) The Use of the central vein sign in the diagnosis of multiple sclerosis: a systematic review and meta-analysis. Diagnostics (Basel) 10 https://doi.org/10.3390/diagnostics10121025
Mainero C, Benner T, Radding A et al (2009) In vivo imaging of cortical pathology in multiple sclerosis using ultra-high field MRI. Neurology. 73:941–948 https://doi.org/10.1212/WNL.0b013e3181b64bf7
Article
PubMed
PubMed Central
Google Scholar
Cocozza S, Cosottini M, Signori A et al (2020) A clinically feasible 7-Tesla protocol for the identification of cortical lesions in Multiple Sclerosis. Eur Radiol 30:4586–4594 https://doi.org/10.1007/s00330-020-06803-y
Article
PubMed
Google Scholar
Maranzano J, Dadar M, Rudko DA et al (2019) Comparison of multiple sclerosis cortical lesion types detected by multicontrast 3T and 7T MRI. AJNR Am J Neuroradiol 40:1162–1169 https://doi.org/10.3174/ajnr.A6099
Article
CAS
PubMed
PubMed Central
Google Scholar
Treaba CA, Granberg TE, Sormani MP et al (2019) Longitudinal characterization of cortical lesion development and evolution in multiple sclerosis with 7.0-T MRI. Radiology. 291:740–749 https://doi.org/10.1148/radiol.2019181719
Article
PubMed
Google Scholar
Louapre C, Treaba CA, Barletta V, Mainero C (2020) Ultra-high field 7 T imaging in multiple sclerosis. Curr Opin Neurol 33:422–429 https://doi.org/10.1097/WCO.0000000000000839
Article
PubMed
Google Scholar
Inglese M, Fleysher L, Oesingmann N, Petracca M (2018) Clinical applications of ultra-high field magnetic resonance imaging in multiple sclerosis. Expert Rev Neurother 18:221–230 https://doi.org/10.1080/14737175.2018.1433033
Article
CAS
PubMed
PubMed Central
Google Scholar
van der Kolk AG, Hendrikse J, Zwanenburg JJ, Visser F, Luijten PR (2013) Clinical applications of 7 T MRI in the brain. Eur J Radiol 82:708–718 https://doi.org/10.1016/j.ejrad.2011.07.007
Article
PubMed
Google Scholar
Chou IJ, Lim SY, Tanasescu R et al (2018) Seven-tesla magnetization transfer imaging to detect multiple sclerosis white matter lesions. J Neuroimaging 28:183–190 https://doi.org/10.1111/jon.12474
Article
PubMed
Google Scholar
Fartaria MJ, Sati P, Todea A et al (2019) Automated detection and segmentation of multiple sclerosis lesions using ultra-high-field MP2RAGE. Investig Radiol 54:356–364 https://doi.org/10.1097/RLI.0000000000000551
Article
Google Scholar
Dula AN, Pawate S, Dortch RD et al (2016) Magnetic resonance imaging of the cervical spinal cord in multiple sclerosis at 7T. Mult Scler 22:320–328 https://doi.org/10.1177/1352458515591070
Article
CAS
PubMed
Google Scholar
Ouellette R, Treaba CA, Granberg T et al (2020) 7 T imaging reveals a gradient in spinal cord lesion distribution in multiple sclerosis. Brain. 143:2973–2987 https://doi.org/10.1093/brain/awaa249
Article
PubMed
PubMed Central
Google Scholar
Paek SL, Chung YS, Paek SH et al (2013) Early experience of pre- and post-contrast 7.0T MRI in brain tumors. J Korean Med Sci 28:1362–1372 https://doi.org/10.3346/jkms.2013.28.9.1362
Article
PubMed
PubMed Central
Google Scholar
Moenninghoff C, Maderwald S, Theysohn JM et al (2010) Imaging of adult astrocytic brain tumours with 7 T MRI: preliminary results. Eur Radiol 20:704–713 https://doi.org/10.1007/s00330-009-1592-2
Article
PubMed
Google Scholar
Rutland JW, Delman BN, Gill CM, Zhu C, Shrivastava RK, Balchandani P (2020) Emerging use of ultra-high-field 7T MRI in the study of intracranial vascularity: state of the field and future directions. AJNR Am J Neuroradiol 41:2–9 https://doi.org/10.3174/ajnr.A6344
Article
CAS
PubMed
PubMed Central
Google Scholar
Morrison MA, Lupo JM (2021) 7-T magnetic resonance imaging in the management of brain tumors. Magn Reson Imaging Clin N Am 29:83–102 https://doi.org/10.1016/j.mric.2020.09.007
Article
PubMed
Google Scholar
Grabner G, Kiesel B, Wohrer A et al (2017) Local image variance of 7 Tesla SWI is a new technique for preoperative characterization of diffusely infiltrating gliomas: correlation with tumour grade and IDH1 mutational status. Eur Radiol 27:1556–1567 https://doi.org/10.1007/s00330-016-4451-y
Article
PubMed
Google Scholar
Christoforidis GA, Yang M, Abduljalil A et al (2012) “Tumoral pseudoblush” identified within gliomas at high-spatial-resolution ultrahigh-field-strength gradient-echo MR imaging corresponds to microvascularity at stereotactic biopsy. Radiology. 264:210–217 https://doi.org/10.1148/radiol.12110799
Article
PubMed
PubMed Central
Google Scholar
Radbruch A, Eidel O, Wiestler B et al (2014) Quantification of tumor vessels in glioblastoma patients using time-of-flight angiography at 7 Tesla: a feasibility study. PLoS One 9:e110727 https://doi.org/10.1371/journal.pone.0110727
Article
PubMed
PubMed Central
CAS
Google Scholar
Grabner G, Nobauer I, Elandt K et al (2012) Longitudinal brain imaging of five malignant glioma patients treated with bevacizumab using susceptibility-weighted magnetic resonance imaging at 7 T. Magn Reson Imaging 30:139–147 https://doi.org/10.1016/j.mri.2011.08.004
Article
CAS
PubMed
Google Scholar
Regnery S, Knowles BR, Paech D et al (2019) High-resolution FLAIR MRI at 7 Tesla for treatment planning in glioblastoma patients. Radiother Oncol 130:180–184 https://doi.org/10.1016/j.radonc.2018.08.002
Article
PubMed
Google Scholar
Regnery S, Behl NGR, Platt T et al (2020) Ultra-high-field sodium MRI as biomarker for tumor extent, grade and IDH mutation status in glioma patients. Neuroimage Clin 28:102427 https://doi.org/10.1016/j.nicl.2020.102427
Article
PubMed
PubMed Central
Google Scholar
Hangel G, Cadrien C, Lazen P et al (2020) High-resolution metabolic imaging of high-grade gliomas using 7T-CRT-FID-MRSI. Neuroimage Clin 28:102433 https://doi.org/10.1016/j.nicl.2020.102433
Article
PubMed
PubMed Central
Google Scholar
Ladd ME, Bachert P, Meyerspeer M et al (2018) Pros and cons of ultra-high-field MRI/MRS for human application. Prog Nucl Magn Reson Spectrosc 109:1–50 https://doi.org/10.1016/j.pnmrs.2018.06.001
Article
CAS
PubMed
Google Scholar
Moser E, Stahlberg F, Ladd ME, Trattnig S (2012) 7-T MR--from research to clinical applications? NMR Biomed 25:695–716 https://doi.org/10.1002/nbm.1794
Article
PubMed
Google Scholar
Li Y, Larson P, Chen AP et al (2015) Short-echo three-dimensional H-1 MR spectroscopic imaging of patients with glioma at 7 Tesla for characterization of differences in metabolite levels. J Magn Reson Imaging 41:1332–1341 https://doi.org/10.1002/jmri.24672
Article
PubMed
Google Scholar
An Z, Tiwari V, Ganji SK et al (2018) Echo-planar spectroscopic imaging with dual-readout alternated gradients (DRAG-EPSI) at 7 T: Application for 2-hydroxyglutarate imaging in glioma patients. Magn Reson Med 79:1851–1861 https://doi.org/10.1002/mrm.26884
Article
CAS
PubMed
Google Scholar
Bogner W, Otazo R, Henning A (2020) Accelerated MR spectroscopic imaging-a review of current and emerging techniques. NMR Biomed:e4314. https://doi.org/10.1002/nbm.4314
Hangel G, Jain S, Springer E et al (2019) High-resolution metabolic mapping of gliomas via patch-based super-resolution magnetic resonance spectroscopic imaging at 7T. Neuroimage. 191:587–595 https://doi.org/10.1016/j.neuroimage.2019.02.023
Article
PubMed
Google Scholar
Hingerl L, Strasser B, Moser P et al (2020) Clinical high-resolution 3D-MR spectroscopic imaging of the human brain at 7 T. Investig Radiol 55:239–248 https://doi.org/10.1097/RLI.0000000000000626
Article
CAS
Google Scholar
Berrington A, Voets NL, Larkin SJ et al (2018) A comparison of 2-hydroxyglutarate detection at 3 and 7 T with long-TE semi-LASER. NMR Biomed 31 https://doi.org/10.1002/nbm.3886
Bisdas S, Chadzynski GL, Braun C et al (2016) MR spectroscopy for in vivo assessment of the oncometabolite 2-hydroxyglutarate and its effects on cellular metabolism in human brain gliomas at 9.4T. J Magn Reson Imaging 44:823–833 https://doi.org/10.1002/jmri.25221
Article
PubMed
Google Scholar
Dreher C, Oberhollenzer J, Meissner JE et al (2019) Chemical exchange saturation transfer (CEST) signal intensity at 7T MRI of WHO IV degrees gliomas is dependent on the anatomic location. J Magn Reson Imaging 49:777–785 https://doi.org/10.1002/jmri.26215
Article
PubMed
Google Scholar
Khlebnikov V, van der Kemp WJM, Hoogduin H, Klomp DWJ, Prompers JJ (2019) Analysis of chemical exchange saturation transfer contributions from brain metabolites to the Z-spectra at various field strengths and pH. Sci Rep 9:1089 https://doi.org/10.1038/s41598-018-37295-y
Article
PubMed
PubMed Central
CAS
Google Scholar
Meissner JE, Korzowski A, Regnery S et al (2019) Early response assessment of glioma patients to definitive chemoradiotherapy using chemical exchange saturation transfer imaging at 7 T. J Magn Reson Imaging 50:1268–1277 https://doi.org/10.1002/jmri.26702
Article
PubMed
Google Scholar
Paech D, Dreher C, Regnery S et al (2019) Relaxation-compensated amide proton transfer (APT) MRI signal intensity is associated with survival and progression in high-grade glioma patients. Eur Radiol 29:4957–4967 https://doi.org/10.1007/s00330-019-06066-2
Article
PubMed
Google Scholar
Paech D, Windschuh J, Oberhollenzer J et al (2018) Assessing the predictability of IDH mutation and MGMT methylation status in glioma patients using relaxation-compensated multipool CEST MRI at 7.0 T. Neuro-Oncology 20:1661–1671 https://doi.org/10.1093/neuonc/noy073
Article
CAS
PubMed
PubMed Central
Google Scholar
Regnery S, Adeberg S, Dreher C et al (2018) Chemical exchange saturation transfer MRI serves as predictor of early progression in glioblastoma patients. Oncotarget 9:28772–28783 https://doi.org/10.18632/oncotarget.25594
Article
PubMed
PubMed Central
Google Scholar
Bulk M, Abdelmoula WM, Nabuurs RJA et al (2018) Postmortem MRI and histology demonstrate differential iron accumulation and cortical myelin organization in early- and late-onset Alzheimer’s disease. Neurobiol Aging 62:231–242 https://doi.org/10.1016/j.neurobiolaging.2017.10.017
Article
CAS
PubMed
Google Scholar
Bulk M, Kenkhuis B, van der Graaf LM, Goeman JJ, Natte R, van der Weerd L (2018) Postmortem T2*- weighted MRI imaging of cortical iron reflects severity of Alzheimer’s disease. J Alzheimers Dis 65:1125–1137 https://doi.org/10.3233/JAD-180317
Article
CAS
PubMed
PubMed Central
Google Scholar
Kenkhuis B, Jonkman LE, Bulk M et al (2019) 7T MRI allows detection of disturbed cortical lamination of the medial temporal lobe in patients with Alzheimer's disease. Neuroimage Clin 21:101665 https://doi.org/10.1016/j.nicl.2019.101665
Article
PubMed
PubMed Central
Google Scholar
van Bergen JM, Li X, Hua J et al (2016) Colocalization of cerebral iron with Amyloid beta in Mild Cognitive Impairment. Sci Rep 6:35514 https://doi.org/10.1038/srep35514
Article
PubMed
PubMed Central
CAS
Google Scholar
van Rooden S, Doan NT, Versluis MJ et al (2015) 7T T(2)*-weighted magnetic resonance imaging reveals cortical phase differences between early- and late-onset Alzheimer's disease. Neurobiol Aging 36:20–26 https://doi.org/10.1016/j.neurobiolaging.2014.07.006
Article
PubMed
Google Scholar
Kerchner GA, Bernstein JD, Fenesy MC et al (2013) Shared vulnerability of two synaptically-connected medial temporal lobe areas to age and cognitive decline: a seven tesla magnetic resonance imaging study. J Neurosci 33:16666–16672 https://doi.org/10.1523/JNEUROSCI.1915-13.2013
Article
CAS
PubMed
PubMed Central
Google Scholar
Kerchner GA, Deutsch GK, Zeineh M, Dougherty RF, Saranathan M, Rutt BK (2012) Hippocampal CA1 apical neuropil atrophy and memory performance in Alzheimer’s disease. Neuroimage. 63:194–202 https://doi.org/10.1016/j.neuroimage.2012.06.048
Article
PubMed
Google Scholar
Kerchner GA, Hess CP, Hammond-Rosenbluth KE et al (2010) Hippocampal CA1 apical neuropil atrophy in mild Alzheimer disease visualized with 7-T MRI. Neurology. 75:1381–1387 https://doi.org/10.1212/WNL.0b013e3181f736a1
Article
CAS
PubMed
PubMed Central
Google Scholar
Wisse LE, Biessels GJ, Heringa SM et al (2014) Hippocampal subfield volumes at 7T in early Alzheimer’s disease and normal aging. Neurobiol Aging 35:2039–2045 https://doi.org/10.1016/j.neurobiolaging.2014.02.021
Article
PubMed
Google Scholar
Bouvy WH, van Veluw SJ, Kuijf HJ et al (2020) Microbleeds colocalize with enlarged juxtacortical perivascular spaces in amnestic mild cognitive impairment and early Alzheimer’s disease: A 7 Tesla MRI study. J Cereb Blood Flow Metab 40:739–746 https://doi.org/10.1177/0271678X19838087
Article
CAS
PubMed
Google Scholar
Welter ML, Schupbach M, Czernecki V et al (2014) Optimal target localization for subthalamic stimulation in patients with Parkinson disease. Neurology. 82:1352–1361 https://doi.org/10.1212/WNL.0000000000000315
Article
PubMed
PubMed Central
Google Scholar
Cho ZH, Min HK, Oh SH et al (2010) Direct visualization of deep brain stimulation targets in Parkinson disease with the use of 7-tesla magnetic resonance imaging. J Neurosurg 113:639–647 https://doi.org/10.3171/2010.3.JNS091385
Article
PubMed
PubMed Central
Google Scholar
Abosch A, Yacoub E, Ugurbil K, Harel N (2010) An assessment of current brain targets for deep brain stimulation surgery with susceptibility-weighted imaging at 7 tesla. Neurosurgery. 67:1745–1756 discussion 56. https://doi.org/10.1227/NEU.0b013e3181f74105
Article
PubMed
Google Scholar
Patriat R, Cooper SE, Duchin Y et al (2018) Individualized tractography-based parcellation of the globus pallidus pars interna using 7T MRI in movement disorder patients prior to DBS surgery. Neuroimage. 178:198–209 https://doi.org/10.1016/j.neuroimage.2018.05.048
Article
PubMed
Google Scholar
Plantinga BR, Temel Y, Duchin Y et al (2018) Individualized parcellation of the subthalamic nucleus in patients with Parkinson's disease with 7T MRI. Neuroimage. 168:403–411 https://doi.org/10.1016/j.neuroimage.2016.09.023
Article
PubMed
Google Scholar
Plantinga BR, Temel Y, Roebroeck A et al (2014) Ultra-high field magnetic resonance imaging of the basal ganglia and related structures. Front Hum Neurosci 8:876 https://doi.org/10.3389/fnhum.2014.00876
Article
PubMed
PubMed Central
Google Scholar
Cho ZH, Oh SH, Kim JM et al (2011) Direct visualization of Parkinson’s disease by in vivo human brain imaging using 7.0T magnetic resonance imaging. Mov Disord 26:713–718 https://doi.org/10.1002/mds.23465
Article
PubMed
Google Scholar
Patriat R, Niederer J, Kaplan J et al (2020) Morphological changes in the subthalamic nucleus of people with mild-to-moderate Parkinson’s disease: a 7T MRI study. Sci Rep 10:8785 https://doi.org/10.1038/s41598-020-65752-0
Article
CAS
PubMed
PubMed Central
Google Scholar
Poston KL, Ua Cruadhlaoich MAI, Santoso LF et al (2020) Substantia nigra volume dissociates bradykinesia and rigidity from tremor in Parkinson’s disease: A 7 Tesla Imaging Study. J Parkinsons Dis 10:591–604 https://doi.org/10.3233/JPD-191890
Article
PubMed
PubMed Central
Google Scholar
La C, Linortner P, Bernstein JD et al (2019) Hippocampal CA1 subfield predicts episodic memory impairment in Parkinson's disease. Neuroimage Clin 23:101824 https://doi.org/10.1016/j.nicl.2019.101824
Article
PubMed
PubMed Central
Google Scholar
Madai VI, von Samson-Himmelstjerna FC, Bauer M et al (2012) Ultrahigh-field MRI in human ischemic stroke--a 7 tesla study. PLoS One 7:e37631 https://doi.org/10.1371/journal.pone.0037631
Article
CAS
PubMed
PubMed Central
Google Scholar
De Cocker LJ, Lindenholz A, Zwanenburg JJ et al (2018) Clinical vascular imaging in the brain at 7T. Neuroimage. 168:452–458 https://doi.org/10.1016/j.neuroimage.2016.11.044
Article
PubMed
Google Scholar
Shao X, Yan L, Ma SJ, Wang K, Wang DJJ (2021) High-Resolution Neurovascular Imaging at 7T: Arterial spin labeling perfusion, 4-dimensional MR angiography, and black blood mr imaging. Magn Reson Imaging Clin N Am 29:53–65 https://doi.org/10.1016/j.mric.2020.09.003
Article
PubMed
Google Scholar
Miyazawa H, Natori T, Kameda H et al (2019) Detecting lenticulostriate artery lesions in patients with acute ischemic stroke using high-resolution MRA at 7 T. Int J Stroke 14:290–297 https://doi.org/10.1177/1747493018806163
Article
PubMed
Google Scholar
Kang CK, Park CA, Park CW, Lee YB, Cho ZH, Kim YB (2010) Lenticulostriate arteries in chronic stroke patients visualised by 7 T magnetic resonance angiography. Int J Stroke 5:374–380 https://doi.org/10.1111/j.1747-4949.2010.00464.x
Article
PubMed
Google Scholar
Yaghi S, Prabhakaran S, Khatri P, Liebeskind DS (2019) Intracranial atherosclerotic disease. Stroke. 50:1286–1293 https://doi.org/10.1161/STROKEAHA.118.024147
Article
PubMed
Google Scholar
Lindenholz A, van der Kolk AG, van der Schaaf IC et al (2020) Intracranial atherosclerosis assessed with 7-T MRI: evaluation of patients with ischemic stroke or transient ischemic attack. Radiology. 295:162–170 https://doi.org/10.1148/radiol.2020190643
Article
PubMed
Google Scholar
Lindenholz A, van der Schaaf IC, van der Kolk AG et al (2020) MRI vessel wall imaging after intra-arterial treatment for acute ischemic stroke. AJNR Am J Neuroradiol 41:624–631 https://doi.org/10.3174/ajnr.A6460
Article
CAS
PubMed
PubMed Central
Google Scholar
Zwartbol MHT, Geerlings MI, Ghaznawi R, Hendrikse J, van der Kolk AG, Group U-SS (2019) Intracranial atherosclerotic burden on 7T MRI is associated with markers of extracranial atherosclerosis: the SMART-MR study. AJNR Am J Neuroradiol 40:2016–2022 https://doi.org/10.3174/ajnr.A6308
CAS
PubMed
PubMed Central
Google Scholar
Zhu C, Haraldsson H, Tian B et al (2016) High resolution imaging of the intracranial vessel wall at 3 and 7 T using 3D fast spin echo MRI. MAGMA. 29:559–570 https://doi.org/10.1007/s10334-016-0531-x
Article
CAS
PubMed
Google Scholar
Majidi S, Sein J, Watanabe M et al (2013) Intracranial-derived atherosclerosis assessment: an in vitro comparison between virtual histology by intravascular ultrasonography, 7T MRI, and histopathologic findings. AJNR Am J Neuroradiol 34:2259–2264 https://doi.org/10.3174/ajnr.A3631
Article
CAS
PubMed
PubMed Central
Google Scholar
Sato T, Matsushige T, Chen B et al (2019) Wall contrast enhancement of thrombosed intracranial aneurysms at 7T MRI. AJNR Am J Neuroradiol 40:1106–1111 https://doi.org/10.3174/ajnr.A6084
Article
CAS
PubMed
PubMed Central
Google Scholar
Wrede KH, Dammann P, Monninghoff C et al (2014) Non-enhanced MR imaging of cerebral aneurysms: 7 Tesla versus 1.5 Tesla. PLoS One 9:e84562 https://doi.org/10.1371/journal.pone.0084562
Article
PubMed
PubMed Central
CAS
Google Scholar
Wrede KH, Matsushige T, Goericke SL et al (2017) Non-enhanced magnetic resonance imaging of unruptured intracranial aneurysms at 7 Tesla: comparison with digital subtraction angiography. Eur Radiol 27:354–364 https://doi.org/10.1007/s00330-016-4323-5
Article
PubMed
Google Scholar
Van Essen DC, Ugurbil K, Auerbach E et al (2012) The Human Connectome Project: a data acquisition perspective. Neuroimage. 62:2222–2231 https://doi.org/10.1016/j.neuroimage.2012.02.018
Article
PubMed
Google Scholar
Setsompop K, Kimmlingen R, Eberlein E et al (2013) Pushing the limits of in vivo diffusion MRI for the Human Connectome Project. Neuroimage. 80:220–233 https://doi.org/10.1016/j.neuroimage.2013.05.078
Article
CAS
PubMed
Google Scholar
Foo TKF, Tan ET, Vermilyea ME et al (2020) Highly efficient head-only magnetic field insert gradient coil for achieving simultaneous high gradient amplitude and slew rate at 3.0T (MAGNUS) for brain microstructure imaging. Magn Reson Med 83:2356–2369 https://doi.org/10.1002/mrm.28087
Article
PubMed
Google Scholar
Winkler SA, Schmitt F, Landes H et al (2018) Gradient and shim technologies for ultra high field MRI. Neuroimage. 168:59–70 https://doi.org/10.1016/j.neuroimage.2016.11.033
Article
PubMed
Google Scholar
Stockmann JP, Wald LL (2018) In vivo B0 field shimming methods for MRI at 7T. Neuroimage. 168:71–87 https://doi.org/10.1016/j.neuroimage.2017.06.013
Article
PubMed
Google Scholar
Polimeni JR, Wald LL (2018) Magnetic resonance imaging technology-bridging the gap between noninvasive human imaging and optical microscopy. Curr Opin Neurobiol 50:250–260 https://doi.org/10.1016/j.conb.2018.04.026
Article
CAS
PubMed
PubMed Central
Google Scholar
Davids M, Guerin B, Vom Endt A, Schad LR, Wald LL (2019) Prediction of peripheral nerve stimulation thresholds of MRI gradient coils using coupled electromagnetic and neurodynamic simulations. Magn Reson Med 81:686–701 https://doi.org/10.1002/mrm.27382
Article
PubMed
Google Scholar
Davids M, Guerin B, Klein V, Wald LL (2020) Optimization of MRI gradient coils with explicit peripheral nerve stimulation constraints. IEEE Trans Med Imaging https://doi.org/10.1109/TMI.2020.3023329
Tan ET, Hua Y, Fiveland EW et al (2020) Peripheral nerve stimulation limits of a high amplitude and slew rate magnetic field gradient coil for neuroimaging. Magn Reson Med 83:352–366 https://doi.org/10.1002/mrm.27909
Article
PubMed
Google Scholar
Meyer CH, Hu BS, Nishimura DG, Macovski A (1992) Fast spiral coronary artery imaging. Magn Reson Med 28:202–213 https://doi.org/10.1002/mrm.1910280204
Article
CAS
PubMed
Google Scholar
Frahm J, Voit D, Uecker M (2019) Real-time magnetic resonance imaging: radial gradient-echo sequences with nonlinear inverse reconstruction. Investig Radiol 54:757–766 https://doi.org/10.1097/RLI.0000000000000584
Article
Google Scholar
Bilgic B, Gagoski BA, Cauley SF et al (2015) Wave-CAIPI for highly accelerated 3D imaging. Magn Reson Med 73:2152–2162 https://doi.org/10.1002/mrm.25347
Article
PubMed
Google Scholar
Cauley SF, Setsompop K, Bilgic B, Bhat H, Gagoski B, Wald LL (2017) Autocalibrated wave-CAIPI reconstruction; Joint optimization of k-space trajectory and parallel imaging reconstruction. Magn Reson Med 78:1093–1099 https://doi.org/10.1002/mrm.26499
Article
CAS
PubMed
Google Scholar
Polak D, Cauley S, Huang SY et al (2019) Highly-accelerated volumetric brain examination using optimized wave-CAIPI encoding. J Magn Reson Imaging 50:961–974 https://doi.org/10.1002/jmri.26678
Article
PubMed
PubMed Central
Google Scholar
Conklin J, Longo MGF, Cauley SF et al (2019) Validation of highly accelerated wave-CAIPI SWI compared with conventional SWI and T2*-weighted gradient recalled-echo for routine clinical brain MRI at 3T. AJNR Am J Neuroradiol 40:2073–2080 https://doi.org/10.3174/ajnr.A6295
CAS
PubMed
PubMed Central
Google Scholar
Goncalves Filho ALM, Conklin J, Longo MGF et al (2020) Accelerated post-contrast wave-CAIPI T1 SPACE achieves equivalent diagnostic performance compared with standard T1 SPACE for the detection of brain metastases in clinical 3T MRI. Front Neurol 11:587327 https://doi.org/10.3389/fneur.2020.587327
Article
PubMed
PubMed Central
Google Scholar
Longo MGF, Conklin J, Cauley SF et al (2020) Evaluation of ultrafast wave-CAIPI MPRAGE for visual grading and automated measurement of brain tissue volume. AJNR Am J Neuroradiol 41:1388–1396 https://doi.org/10.3174/ajnr.A6703
Article
CAS
PubMed
PubMed Central
Google Scholar
McNab JA, Edlow BL, Witzel T et al (2013) The human connectome project and beyond: initial applications of 300 mT/m gradients. Neuroimage. 80:234–245 https://doi.org/10.1016/j.neuroimage.2013.05.074
Article
PubMed
Google Scholar
Fan Q, Nummenmaa A, Witzel T et al (2014) Investigating the capability to resolve complex white matter structures with high b-value diffusion magnetic resonance imaging on the MGH-USC Connectom scanner. Brain Connect 4:718–726 https://doi.org/10.1089/brain.2014.0305
Article
PubMed
PubMed Central
Google Scholar
Fan Q, Nummenmaa A, Polimeni JR et al (2017) HIgh b-value and high Resolution Integrated Diffusion (HIBRID) imaging. Neuroimage. 150:162–176 https://doi.org/10.1016/j.neuroimage.2017.02.002
Article
PubMed
Google Scholar
Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 61:1000–1016 https://doi.org/10.1016/j.neuroimage.2012.03.072
Article
PubMed
Google Scholar
Assaf Y, Blumenfeld-Katzir T, Yovel Y, Basser PJ (2008) AxCaliber: a method for measuring axon diameter distribution from diffusion MRI. Magn Reson Med 59:1347–1354 https://doi.org/10.1002/mrm.21577
Article
PubMed
PubMed Central
Google Scholar
Alexander DC, Hubbard PL, Hall MG et al (2010) Orientationally invariant indices of axon diameter and density from diffusion MRI. Neuroimage. 52:1374–1389 https://doi.org/10.1016/j.neuroimage.2010.05.043
Article
PubMed
Google Scholar
Dyrby TB, Sogaard LV, Hall MG, Ptito M, Alexander DC (2013) Contrast and stability of the axon diameter index from microstructure imaging with diffusion MRI. Magn Reson Med 70:711–721 https://doi.org/10.1002/mrm.24501
Article
PubMed
Google Scholar
Huang SY, Nummenmaa A, Witzel T et al (2015) The impact of gradient strength on in vivo diffusion MRI estimates of axon diameter. Neuroimage. 106:464–472 https://doi.org/10.1016/j.neuroimage.2014.12.008
Article
PubMed
Google Scholar
Fan Q, Nummenmaa A, Wichtmann B et al (2018) Validation of diffusion MRI estimates of compartment size and volume fraction in a biomimetic brain phantom using a human MRI scanner with 300mT/m maximum gradient strength. Neuroimage. 182:469–478 https://doi.org/10.1016/j.neuroimage.2018.01.004
Article
PubMed
Google Scholar
Lee HH, Fieremans E, Novikov DS (2018) What dominates the time dependence of diffusion transverse to axons: Intra- or extra-axonal water? Neuroimage. 182:500–510 https://doi.org/10.1016/j.neuroimage.2017.12.038
Article
PubMed
Google Scholar
Veraart J, Nunes D, Rudrapatna U et al (2020) Nonivasive quantification of axon radii using diffusion MRI. Elife. 9 https://doi.org/10.7554/eLife.49855
Huang SY, Tian Q, Fan Q et al (2020) High-gradient diffusion MRI reveals distinct estimates of axon diameter index within different white matter tracts in the in vivo human brain. Brain Struct Funct 225:1277–1291 https://doi.org/10.1007/s00429-019-01961-2
Article
PubMed
Google Scholar
Fan Q, Nummenmaa A, Witzel T et al (2020) Axon diameter index estimation independent of fiber orientation distribution using high-gradient diffusion MRI. Neuroimage 222:117197 https://doi.org/10.1016/j.neuroimage.2020.117197
Article
PubMed
Google Scholar
Huang SY, Tobyne SM, Nummenmaa A et al (2016) Characterization of axonal disease in patients with multiple sclerosis using high-gradient-diffusion MR imaging. Radiology:151582 https://doi.org/10.1148/radiol.2016151582
Yu F, Fan Q, Tian Q et al (2019) Imaging G-ratio in multiple sclerosis using high-gradient diffusion MRI and macromolecular tissue volume. AJNR Am J Neuroradiol 40:1871–1877 https://doi.org/10.3174/ajnr.A6283
CAS
PubMed
PubMed Central
Google Scholar
Ngamsombat C, Tian Q, Fan Q et al (2020) Axonal damage in the optic radiation assessed by white matter tract integrity metrics is associated with retinal thinning in multiple sclerosis. Neuroimage Clin 27:102293 https://doi.org/10.1016/j.nicl.2020.102293
Article
PubMed
PubMed Central
Google Scholar
Evangelou N, Esiri MM, Smith S, Palace J, Matthews PM (2000) Quantitative pathological evidence for axonal loss in normal appearing white matter in multiple sclerosis. Ann Neurol 47:391–395
Article
CAS
PubMed
Google Scholar
Huang SY, Fan Q, Machado N et al (2019) Corpus callosum axon diameter relates to cognitive impairment in multiple sclerosis. Ann Clin Transl Neurol 6:882–892 https://doi.org/10.1002/acn3.760
Article
PubMed
PubMed Central
Google Scholar
Fan Q, Tian Q, Ohringer NA et al (2019) Age-related alterations in axonal microstructure in the corpus callosum measured by high-gradient diffusion MRI. Neuroimage. 191:325–336 https://doi.org/10.1016/j.neuroimage.2019.02.036
Article
PubMed
Google Scholar
Salat DH, Tuch DS, Greve DN et al (2005) Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol Aging 26:1215–1227 https://doi.org/10.1016/j.neurobiolaging.2004.09.017
Article
CAS
PubMed
Google Scholar
Tan ET, Shih RY, Mitra J et al (2020) Oscillating diffusion-encoding with a high gradient-amplitude and high slew-rate head-only gradient for human brain imaging. Magn Reson Med 84:950–965 https://doi.org/10.1002/mrm.28180
Article
CAS
PubMed
PubMed Central
Google Scholar
Yang G, Tian Q, Leuze C, Wintermark M, McNab JA (2018) Double diffusion encoding MRI for the clinic. Magn Reson Med 80:507–520 https://doi.org/10.1002/mrm.27043
Article
PubMed
Google Scholar
Westin CF, Knutsson H, Pasternak O et al (2016) Q-space trajectory imaging for multidimensional diffusion MRI of the human brain. Neuroimage. 135:345–362 https://doi.org/10.1016/j.neuroimage.2016.02.039
Article
PubMed
Google Scholar
Tax CM, Szczepankiewicz F, Nilsson M, Jones DK (2019) The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain. bioRxiv https://doi.org/10.1101/584730
Szczepankiewicz F, Lasic S, van Westen D et al (2015) Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors. Neuroimage. 104:241–252 https://doi.org/10.1016/j.neuroimage.2014.09.057
Article
PubMed
Google Scholar
Lewis LD, Setsompop K, Rosen BR, Polimeni JR (2016) Fast fMRI can detect oscillatory neural activity in humans. Proc Natl Acad Sci U S A 113:E6679–E6E85 https://doi.org/10.1073/pnas.1608117113
Article
CAS
PubMed
PubMed Central
Google Scholar
Lewis LD, Setsompop K, Rosen BR, Polimeni JR (2018) Stimulus-dependent hemodynamic response timing across the human subcortical-cortical visual pathway identified through high spatiotemporal resolution 7T fMRI. Neuroimage. 181:279–291 https://doi.org/10.1016/j.neuroimage.2018.06.056
Article
PubMed
Google Scholar