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Journal Abstract Search

132 related items for PubMed ID: 32298959

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  • 3. Comparison of two MR grading systems for correlation between grade of cervical neural foraminal stenosis and clinical manifestations.
    Lee KH, Park HJ, Lee SY, Chung EC, Rho MH, Shin H, Kwon YJ.
    Br J Radiol; 2016 Jun; 89(1062):20150971. PubMed ID: 27007709
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  • 4. The clinical correlation of a new practical MRI method for grading cervical neural foraminal stenosis based on oblique sagittal images.
    Park HJ, Kim SS, Han CH, Lee SY, Chung EC, Kim MS, Kwon HJ.
    AJR Am J Roentgenol; 2014 Aug; 203(2):412-7. PubMed ID: 25055278
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  • 5. A practical MRI grading system for cervical foraminal stenosis based on oblique sagittal images.
    Park HJ, Kim SS, Lee SY, Park NH, Chung EC, Rho MH, Kwon HJ, Kook SH.
    Br J Radiol; 2013 May; 86(1025):20120515. PubMed ID: 23410800
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  • 6. Clinical correlation of a new practical MRI method for assessing cervical spinal canal compression.
    Park HJ, Kim SS, Chung EC, Lee SY, Park NH, Rho MH, Choi SH.
    AJR Am J Roentgenol; 2012 Aug; 199(2):W197-201. PubMed ID: 22826421
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  • 7. Clinical correlation of a new practical MRI method for assessing central lumbar spinal stenosis.
    Park HJ, Kim SS, Lee YJ, Lee SY, Park NH, Choi YJ, Chung EC, Rho MH.
    Br J Radiol; 2013 May; 86(1025):20120180. PubMed ID: 23426848
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  • 10. A New MRI Grading System for Cervical Foraminal Stenosis Based on Axial T2-Weighted Images.
    Kim S, Lee JW, Chai JW, Yoo HJ, Kang Y, Seo J, Ahn JM, Kang HS.
    Korean J Radiol; 2015 May; 16(6):1294-302. PubMed ID: 26576119
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  • 11. Comparison of MRI grading for cervical neural foraminal stenosis based on axial and oblique sagittal images: Concordance and reliability study.
    Kim W, Ahn KS, Kang CH, Kang WY, Yang KS.
    Clin Imaging; 2017 May; 43():165-169. PubMed ID: 28334616
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  • 13. Is multidetector computed tomography comparable to magnetic resonance imaging for assessment of lumbar foraminal stenosis?
    Kang WY, Ahn JM, Lee JW, Lee E, Bae YJ, Seo J, Kim J, Kang HS.
    Acta Radiol; 2017 Feb; 58(2):197-203. PubMed ID: 27055917
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  • 15. Deep Learning Model for Automated Detection and Classification of Central Canal, Lateral Recess, and Neural Foraminal Stenosis at Lumbar Spine MRI.
    Hallinan JTPD, Zhu L, Yang K, Makmur A, Algazwi DAR, Thian YL, Lau S, Choo YS, Eide SE, Yap QV, Chan YH, Tan JH, Kumar N, Ooi BC, Yoshioka H, Quek ST.
    Radiology; 2021 Jul; 300(1):130-138. PubMed ID: 33973835
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  • 16. Comparison of inter- and intra-observer reliability among the three classification systems for cervical spinal canal stenosis.
    Ko S, Choi W, Chae S.
    Eur Spine J; 2017 Sep; 26(9):2290-2296. PubMed ID: 28612191
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