Skip to main content
Advertisement

Main menu

  • Home
  • Content
    • Current Issue
    • Accepted Manuscripts
    • Article Preview
    • Past Issue Archive
    • Video Articles
    • AJNR Case Collection
    • Case of the Week Archive
    • Case of the Month Archive
    • Classic Case Archive
  • Special Collections
    • AJNR Awards
    • Low-Field MRI
    • Alzheimer Disease
    • ASNR Foundation Special Collection
    • Photon-Counting CT
    • View All
  • Multimedia
    • AJNR Podcasts
    • AJNR SCANtastic
    • Trainee Corner
    • MRI Safety Corner
    • Imaging Protocols
  • For Authors
    • Submit a Manuscript
    • Submit a Video Article
    • Submit an eLetter to the Editor/Response
    • Manuscript Submission Guidelines
    • Statistical Tips
    • Fast Publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Imaging Protocol Submission
    • Author Policies
  • About Us
    • About AJNR
    • Editorial Board
    • Editorial Board Alumni
  • More
    • Become a Reviewer/Academy of Reviewers
    • Subscribers
    • Permissions
    • Alerts
    • Feedback
    • Advertisers
    • ASNR Home

User menu

  • Alerts
  • Log in

Search

  • Advanced search
American Journal of Neuroradiology
American Journal of Neuroradiology

American Journal of Neuroradiology

ASHNR American Society of Functional Neuroradiology ASHNR American Society of Pediatric Neuroradiology ASSR
  • Alerts
  • Log in

Advanced Search

  • Home
  • Content
    • Current Issue
    • Accepted Manuscripts
    • Article Preview
    • Past Issue Archive
    • Video Articles
    • AJNR Case Collection
    • Case of the Week Archive
    • Case of the Month Archive
    • Classic Case Archive
  • Special Collections
    • AJNR Awards
    • Low-Field MRI
    • Alzheimer Disease
    • ASNR Foundation Special Collection
    • Photon-Counting CT
    • View All
  • Multimedia
    • AJNR Podcasts
    • AJNR SCANtastic
    • Trainee Corner
    • MRI Safety Corner
    • Imaging Protocols
  • For Authors
    • Submit a Manuscript
    • Submit a Video Article
    • Submit an eLetter to the Editor/Response
    • Manuscript Submission Guidelines
    • Statistical Tips
    • Fast Publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Imaging Protocol Submission
    • Author Policies
  • About Us
    • About AJNR
    • Editorial Board
    • Editorial Board Alumni
  • More
    • Become a Reviewer/Academy of Reviewers
    • Subscribers
    • Permissions
    • Alerts
    • Feedback
    • Advertisers
    • ASNR Home
  • Follow AJNR on Twitter
  • Visit AJNR on Facebook
  • Follow AJNR on Instagram
  • Join AJNR on LinkedIn
  • RSS Feeds

AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Review ArticleReview Article

Head CT: Toward Making Full Use of the Information the X-Rays Give

K.A. Cauley, Y. Hu and S.W. Fielden
American Journal of Neuroradiology August 2021, 42 (8) 1362-1369; DOI: https://doi.org/10.3174/ajnr.A7153
K.A. Cauley
aFrom the Department of Radiology (K.A.C.), Geisinger Medical Center, Danville, Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for K.A. Cauley
Y. Hu
bDepartment of Biomedical & Translational Informatics (Y.H.), Geisinger Medical Center, Danville, Pennsylvania
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Y. Hu
S.W. Fielden
cGeisinger Autism & Developmental Medicine Institute (S.W.F.), Lewisburg, Pennsylvania.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for S.W. Fielden
  • Article
  • Figures & Data
  • Info & Metrics
  • Responses
  • References
  • PDF
Loading

References

  1. 1.↵
    1. Hounsfield GN
    . Computed medical imaging. Nobel lecture, December 8, 1979. J Comput Assist Tomogr 1980;4:665–74 doi:10.1097/00004728-198010000-00017 pmid:6997341
    CrossRefPubMedWeb of Science
  2. 2.↵
    1. Hess EH,
    2. Shah ND,
    3. Stroebel RJ, et al
    . Trends in computed tomography utilization rates: a longitudinal practice-based study. J Patient Saf 2014;10:52–58 doi:10.1097/PTS.0b013e3182948b1a pmid:24080717
    CrossRefPubMed
  3. 3.↵
    The IMV Medical Imaging MasterFile. IMV. https://imvinfo.com/. Accessed May 22, 2021
  4. 4.↵
    1. Jenkinson M,
    2. Beckmann CF,
    3. Behrens TE, et al
    . FSL. Neuroimage 2012;62:782–90 doi:10.1016/j.neuroimage.2011.09.015 pmid:21979382
    CrossRefPubMedWeb of Science
  5. 5.↵
    1. Muschelli J,
    2. Ullman NL,
    3. Mould WA, et al
    . Validated automatic brain extraction of head CT images. Neuroimage 2015;114:379–85 doi:10.1016/j.neuroimage.2015.03.074 pmid:25862260
    CrossRefPubMed
  6. 6.↵
    1. Cauley KA,
    2. Och J,
    3. Yorks PJ, et al
    . Automated segmentation of head computed tomography images using FSL. J Comput Assist Tomogr 2018;42:104–10 doi:10.1097/RCT.0000000000000660 pmid:28786900
    CrossRefPubMed
  7. 7.↵
    1. Cauley KA,
    2. Hu Y,
    3. Fielden SW
    . Aging and the brain: a quantitative study of clinical CT images. AJNR Am J Neuroradiol 2020;41:809–14 doi:10.3174/ajnr.A6510 pmid:32327433
    Abstract/FREE Full Text
  8. 8.↵
    1. Rudick RA,
    2. Fisher E,
    3. Lee JC, et al
    . Use of the brain parenchymal fraction to measure whole brain atrophy in relapsing-remitting MS. Neurology 1999;53:1698–704 doi:10.1212/wnl.53.8.1698 pmid:10563615
    CrossRefPubMed
  9. 9.↵
    1. Blatter DD,
    2. Bigler ED,
    3. Gale SD, et al
    . Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life. AJNR Am J Neuroradiol 1995;16:241–51 pmid:7726068
    Abstract/FREE Full Text
  10. 10.↵
    1. Condon B,
    2. Grant R,
    3. Hadley D, et al
    . Brain and intracranial cavity volumes: in vivo determination by MRI. Acta Neurol Scand 1988;78:387–93 doi:10.1111/j.1600-0404.1988.tb03674.x pmid:3218445
    CrossRefPubMedWeb of Science
  11. 11.↵
    1. Courchesne E,
    2. Chisum HJ,
    3. Townsend J, et al
    . Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology 2000;216:672–82 doi:10.1148/radiology.216.3.r00au37672 pmid:10966694
    CrossRefPubMedWeb of Science
  12. 12.↵
    1. Matsumae M,
    2. Kikinis R,
    3. Mórocz IA, et al
    . Age-related changes in intracranial compartment volumes in normal adults assessed by magnetic resonance imaging. J Neurosurg 1996;84:982–91 doi:10.3171/jns.1996.84.6.0982 pmid:8847593
    CrossRefPubMedWeb of Science
  13. 13.↵
    1. Rajagopalan V,
    2. Pioro EP
    . Brain parenchymal fraction: a relatively simple MRI measure to clinically distinguish ALS phenotypes. Biomed Res Int 2015;2015:693206 doi:10.1155/2015/693206 pmid:26783524
    CrossRefPubMed
  14. 14.↵
    1. Fjell AM,
    2. McEvoy L,
    3. Holland D, et al
    . Brain changes in older adults at very low risk for Alzheimer's disease. J Neurosci 2013;33:8237–42 doi:10.1523/JNEUROSCI.5506-12.2013 pmid:23658162
    Abstract/FREE Full Text
  15. 15.↵
    1. Bin Zahid A,
    2. Mikheev A,
    3. Srivatsa N, et al
    . Accelerated brain atrophy on serial computed tomography: potential marker of the progression of Alzheimer disease. J Comput Assist Tomogr 2016;40:827–32 doi:10.1097/RCT.0000000000000435 pmid:27224227
    CrossRefPubMed
  16. 16.↵
    1. Förstl H,
    2. Zerfass R,
    3. Geiger-Kabisch C, et al
    . Brain atrophy in normal ageing and Alzheimer's disease. Volumetric discrimination and clinical correlations. Br J Psychiatry 1995;167:739–46 doi:10.1192/bjp.167.6.739 pmid:8829740
    Abstract/FREE Full Text
  17. 17.↵
    1. Vågberg M,
    2. Granåsen G,
    3. Svenningsson A
    . Brain parenchymal fraction in healthy adults-a systematic review of the literature. PLoS One 2017;12:e0170018 doi:10.1371/journal.pone.0170018 pmid:28095463
    CrossRefPubMed
  18. 18.↵
    1. Cauley KA,
    2. Hu Y,
    3. Fielden SW
    . Pediatric head CT: automated quantitative analysis with quantile regression. AJNR Am J Neuroradiol 2021;42: 382–88 doi:10.3174/ajnr.A6885 pmid:33303521
    Abstract/FREE Full Text
  19. 19.↵
    1. Haggstrom M
    . Housfield unit. http://radlines.org/Houndfield_unit. Accessed May 22, 2021
  20. 20.↵
    1. Nguyen HS,
    2. Li L,
    3. Patel M, et al
    . Radiodensity of intraventricular hemorrhage associated with aneurysmal subarachnoid hemorrhage may be a negative predictor of outcome. J Neurosurg 2018;128:1032–36 doi:10.3171/2016.11.JNS152839 pmid:28474990
    CrossRefPubMed
  21. 21.↵
    1. Wagner I,
    2. Volbers B,
    3. Hilz MJ, et al
    . Radiopacity of intracerebral hemorrhage correlates with perihemorrhagic edema. Eur J Neurol 2012;19:525–28 doi:10.1111/j.1468-1331.2011.03526.x pmid:21951394
    CrossRefPubMed
  22. 22.↵
    1. Kim H,
    2. Kim GD,
    3. Yoon BC, et al
    . Quantitative analysis of computed tomography images and early detection of cerebral edema for pediatric traumatic brain injury patients: retrospective study. BMC Med 2014;12:186 doi:10.1186/s12916-014-0186-2 pmid:25339549
    CrossRefPubMed
  23. 23.↵
    1. Cauley KA,
    2. Fielden SW
    . A radiodensity histogram study of the brain in multiple sclerosis. Tomography 2018;4:194–203 doi:10.18383/j.tom.2018.00050 pmid:30588505
    CrossRefPubMed
  24. 24.↵
    1. Inaba K,
    2. Teixeira PG,
    3. David JS, et al
    . Computed tomographic brain density measurement as a predictor of elevated intracranial pressure in blunt head trauma. Am Surg 2007;73:1023–26 doi:10.1177/000313480707301022 pmid:17983073
    CrossRefPubMedWeb of Science
  25. 25.↵
    1. Cauley KA,
    2. Hu Y,
    3. Och J, et al
    . Modeling early postnatal brain growth and development with CT: changes in the brain radiodensity histogram from birth to 2 years. AJNR Am J Neuroradiol 2018;39:775–81 doi:10.3174/ajnr.A5559 pmid:29449277
    Abstract/FREE Full Text
  26. 26.↵
    1. Scahill RI,
    2. Frost C,
    3. Jenkins R, et al
    . A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch Neurol 2003;60:989–94 doi:10.1001/archneur.60.7.989 pmid:12873856
    CrossRefPubMedWeb of Science
  27. 27.↵
    1. Despotović I,
    2. Goossens B,
    3. Philips W
    . MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015;2015:450341 doi:10.1155/2015/450341 pmid:25945121
    CrossRefPubMed
  28. 28.↵
    1. Kemmling A,
    2. Wersching H,
    3. Berger K, et al
    . Decomposing the Hounsfield unit: probabilistic segmentation of brain tissue in computed tomography. Clin Neuroradiol 2012;22:79–91 doi:10.1007/s00062-011-0123-0 pmid:22270832
    CrossRefPubMed
  29. 29.↵
    1. Furat OW,
    2. Neumann M,
    3. Petrich M, et al
    . Machine learning techniques for the segmentation of tomographic image data of functional materials. Front Mater 2019;6:1–17 doi:10.3389/fmats.2019.00145
    CrossRef
  30. 30.↵
    1. Eggert LD,
    2. Sommer J,
    3. Jansen A, et al
    . Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain. PLoS One 2012;7:e45081 doi:10.1371/journal.pone.0045081 pmid:23028771
    CrossRefPubMed
  31. 31.↵
    1. Lee BK,
    2. Jeung KW,
    3. Song KH, et al
    . Prognostic values of gray matter to white matter ratios on early brain computed tomography in adult comatose patients after out-of-hospital cardiac arrest of cardiac etiology. Resuscitation 2015;96:46–52 doi:10.1016/j.resuscitation.2015.07.027 pmid:26232516
    CrossRefPubMed
  32. 32.↵
    1. Hanning U,
    2. Sporns PB,
    3. Lebiedz P, et al
    . Automated assessment of early hypoxic brain edema in non-enhanced CT predicts outcome in patients after cardiac arrest. Resuscitation 2016;104:91–94 doi:10.1016/j.resuscitation.2016.03.018 pmid:27036663
    CrossRefPubMed
  33. 33.↵
    1. Berger N,
    2. Ampanozi G,
    3. Schweitzer W, et al
    . Racking the brain: detection of cerebral edema on postmortem computed tomography compared with forensic autopsy. Eur J Radiol 2015;84:643–51 doi:10.1016/j.ejrad.2014.12.014 pmid:25604908
    CrossRefPubMed
  34. 34.↵
    1. Hanzlik E,
    2. Gigante J
    . Microcephaly. Children (Basel) 2017;4:47 doi:10.3390/children4060047 pmid:28598357
    CrossRefPubMed
  35. 35.↵
    1. Purugganan OH
    . Abnormalities in head size. Pediatr Rev 2006;27:473–76 doi:10.1542/pir.27-12-473 pmid:17142470
    FREE Full Text
  36. 36.↵
    1. Gillebert CR,
    2. Humphreys GW,
    3. Mantini D
    . Automated delineation of stroke lesions using brain CT images. Neuroimage Clin 2014;4:540–48 doi:10.1016/j.nicl.2014.03.009 pmid:24818079
    CrossRefPubMed
  37. 37.↵
    1. Di Chiro G,
    2. Brooks RA,
    3. Dubal L, et al
    . The apical artifact: elevated attenuation values toward the apex of the skull. J Comput Assist Tomogr 1978;2:65–70 doi:10.1097/00004728-197801000-00010 pmid:670474
    CrossRefPubMedWeb of Science
  38. 38.↵
    1. Gado M,
    2. Phelps M
    . The peripheral zone of increase density in cranial computed tomography. Radiology 1975;117:71–74 doi:10.1148/117.1.71 pmid:1162076
    CrossRefPubMedWeb of Science
  39. 39.↵
    1. Craddock C,
    2. Chen MY,
    3. Dixon RL, et al
    . The effect of skull volume and density on differentiating gray and white matter on routine computed tomography scans of the head. J Comput Assist Tomogr 2006;30:734–38 doi:10.1097/01.rct.0000216111.16774.d2 pmid:16954919
    CrossRefPubMed
  40. 40.↵
    1. Barrett JF,
    2. Keat N
    . Artifacts in CT: recognition and avoidance. Radiographics 2004;24:1679–91 doi:10.1148/rg.246045065 pmid:15537976
    CrossRefPubMedWeb of Science
  41. 41.↵
    1. Boas EF,
    2. D
    . CT artifacts: causes and reduction techniques. Imaging in Medicine 2012;4:229–40
  42. 42.↵
    1. Cauley KA,
    2. Yorks PJ,
    3. Flora S, et al
    . The effects of the skull on CT imaging of the brain: a skull and brain phantom study. BR J Radiol 2021;94:20200714 doi:10.1259/bjr.20200714 pmid:33533635
    CrossRefPubMed
  43. 43.↵
    1. Hou Z
    . A review on MR image intensity inhomogeneity correction. Int J Biomed Imaging 2006;2006:49515 doi:10.1155/IJBI/2006/49515 pmid:23165035
    CrossRefPubMed
  44. 44.↵
    1. Vovk U,
    2. Pernus F,
    3. Likar B
    . A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 2007;26:405–21 doi:10.1109/TMI.2006.891486 pmid:17354645
    CrossRefPubMedWeb of Science
  45. 45.↵
    1. Choi SP,
    2. Park HK,
    3. Park KN, et al
    . The density ratio of grey to white matter on computed tomography as an early predictor of vegetative state or death after cardiac arrest. Emerg Med J 2008;25:666–69 doi:10.1136/emj.2007.053306 pmid:18843066
    Abstract/FREE Full Text
  46. 46.↵
    1. Metter RB,
    2. Rittenberger JC,
    3. Guyette FX, et al
    . Association between a quantitative CT scan measure of brain edema and outcome after cardiac arrest. Resuscitation 2011;82:1180–85 doi:10.1016/j.resuscitation.2011.04.001 pmid:21592642
    CrossRefPubMed
  47. 47.↵
    1. Torbey MT,
    2. Selim M,
    3. Knorr J, et al
    . Quantitative analysis of the loss of distinction between gray and white matter in comatose patients after cardiac arrest. Stroke 2000;31:2163–67 doi:10.1161/01.STR.31.9.2163 pmid:10978046
    Abstract/FREE Full Text
  48. 48.↵
    1. Carne RP,
    2. Vogrin S,
    3. Litewka L, et al
    . Cerebral cortex: an MRI-based study of volume and variance with age and sex. J Clin Neurosci 2006;13:60–72 doi:10.1016/j.jocn.2005.02.013 pmid:16410199
    CrossRefPubMedWeb of Science
  49. 49.↵
    1. Xu J,
    2. Kobayashi S,
    3. Yamaguchi S, et al
    . Gender effects on age-related changes in brain structure. AJNR Am J Neuroradiol 2000;21:112–18 pmid:10669234
    Abstract/FREE Full Text
  50. 50.↵
    1. Bozzali M,
    2. Cercignani M,
    3. Caltagirone C
    . Brain volumetrics to investigate aging and the principal forms of degenerative cognitive decline: a brief review. Magn Reson Imaging 2008;26:1065–70 doi:10.1016/j.mri.2008.01.044 pmid:18436405
    CrossRefPubMed
  51. 51.↵
    1. Geuze E,
    2. Vermetten E,
    3. Bremner JD
    . MR-based in vivo hippocampal volumetrics: 2. Findings in neuropsychiatric disorders. Mol Psychiatry 2005;10:160–84 doi:10.1038/sj.mp.4001579 pmid:15356639
    CrossRefPubMedWeb of Science
  52. 52.↵
    1. Cai JC,
    2. Akkus Z,
    3. Philbrick KA, et al
    . Fully automated segmentation of head CT neuroanatomy using deep learning. Radiol: Artificial Intelligence 2020;2:e190183 doi:10.1148/ryai.2020190183
    CrossRef
  53. 53.↵
    1. Pessis E,
    2. Campagna R,
    3. Sverzut JM, et al
    . Virtual monochromatic spectral imaging with fast kilovoltage switching: reduction of metal artifacts at CT. Radiographics 2013;33:573–83 doi:10.1148/rg.332125124 pmid:23479714
    CrossRefPubMed
  54. 54.↵
    1. Pomerantz SR,
    2. Kamalian S,
    3. Zhang D, et al
    . Virtual monochromatic reconstruction of dual-energy unenhanced head CT at 65-75 keV maximizes image quality compared with conventional polychromatic CT. Radiology 2013;266:318–25 doi:10.1148/radiol.12111604 pmid:23074259
    CrossRefPubMed
PreviousNext
Back to top

In this issue

American Journal of Neuroradiology: 42 (8)
American Journal of Neuroradiology
Vol. 42, Issue 8
1 Aug 2021
  • Table of Contents
  • Index by author
  • Complete Issue (PDF)
Advertisement
Print
Download PDF
Email Article

Thank you for your interest in spreading the word on American Journal of Neuroradiology.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Head CT: Toward Making Full Use of the Information the X-Rays Give
(Your Name) has sent you a message from American Journal of Neuroradiology
(Your Name) thought you would like to see the American Journal of Neuroradiology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Cite this article
K.A. Cauley, Y. Hu, S.W. Fielden
Head CT: Toward Making Full Use of the Information the X-Rays Give
American Journal of Neuroradiology Aug 2021, 42 (8) 1362-1369; DOI: 10.3174/ajnr.A7153

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
0 Responses
Respond to this article
Share
Bookmark this article
Head CT: Toward Making Full Use of the Information the X-Rays Give
K.A. Cauley, Y. Hu, S.W. Fielden
American Journal of Neuroradiology Aug 2021, 42 (8) 1362-1369; DOI: 10.3174/ajnr.A7153
del.icio.us logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Purchase

Jump to section

  • Article
    • Abstract
    • CONCLUSIONS
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • Responses
  • References
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • No citing articles found.
  • Crossref (8)
  • Google Scholar

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

  • Computational Approaches for Acute Traumatic Brain Injury Image Recognition
    Emily Lin, Esther L. Yuh
    Frontiers in Neurology 2022 13
  • Medical image fusion quality assessment based on conditional generative adversarial network
    Lu Tang, Yu Hui, Hang Yang, Yinghong Zhao, Chuangeng Tian
    Frontiers in Neuroscience 2022 16
  • Toward Head Computed Tomography Image Reconstruction Standardization With Deep-Learning-Assisted Automatic Detection
    Bowen Zheng, Chenxi Huang, Xiangji Chen, Yuemei Luo
    IEEE Transactions on Instrumentation and Measurement 2024 73
  • Extraction of Radiological Characteristics From Free-Text Imaging Reports Using Natural Language Processing Among Patients With Ischemic and Hemorrhagic Stroke: Algorithm Development and Validation
    Enshuo Hsu, Abdulaziz T Bako, Thomas Potter, Alan P Pan, Gavin W Britz, Jonika Tannous, Farhaan S Vahidy
    JMIR AI 2023 2
  • Age- and sex-adjusted CT-based reference values for temporal muscle thickness, cross-sectional area and radiodensity
    Emilia K. Pesonen, Otso Arponen, Jaakko Niinimäki, Nicole Hernández, Lasse Pikkarainen, Sami Tetri, Tommi K. Korhonen
    Scientific Reports 2025 15 1
  • Assessment of image quality and establishment of local acceptable quality dose for computed tomography based on patient effective diameter
    Nada Hasan, Chadia Rizk, Fatema Marzooq, Khalid Khan, Maryam AlKhaja, Esameldeen Babikir
    Journal of Medical Imaging 2024 11 04
  • Computer, Communication, and Signal Processing. Smart Solutions Towards SDG
    K. Gayatri, K. M. Anand Kumar, B. Padmavathi, Shankar
    2025 723
  • Contrast quality control for segmentation task based on deep learning models—Application to stroke lesion in CT imaging
    Juliette Moreau, Laura Mechtouff, David Rousseau, Omer Faruk Eker, Yves Berthezene, Tae-Hee Cho, Carole Frindel
    Frontiers in Neurology 2025 16

More in this TOC Section

Review Article

  • Giant Cell Arteritis: Important Imaging Findings
  • DCE MRI in Spinal Disease Assessment
  • Cerebrovascular Anomalies in Fetal Imaging
Show more Review Article

Adult Brain

  • Diagnostic Neuroradiology of Monoclonal Antibodies
  • Clinical Outcomes After Chiari I Decompression
  • Segmentation of Brain Metastases with BLAST
Show more Adult Brain

Similar Articles

Advertisement

Indexed Content

  • Current Issue
  • Accepted Manuscripts
  • Article Preview
  • Past Issues
  • Editorials
  • Editor's Choice
  • Fellows' Journal Club
  • Letters to the Editor
  • Video Articles

Cases

  • Case Collection
  • Archive - Case of the Week
  • Archive - Case of the Month
  • Archive - Classic Case

More from AJNR

  • Trainee Corner
  • Imaging Protocols
  • MRI Safety Corner

Multimedia

  • AJNR Podcasts
  • AJNR Scantastics

Resources

  • Turnaround Time
  • Submit a Manuscript
  • Submit a Video Article
  • Submit an eLetter to the Editor/Response
  • Manuscript Submission Guidelines
  • Statistical Tips
  • Fast Publishing of Accepted Manuscripts
  • Graphical Abstract Preparation
  • Imaging Protocol Submission
  • Evidence-Based Medicine Level Guide
  • Publishing Checklists
  • Author Policies
  • Become a Reviewer/Academy of Reviewers
  • News and Updates

About Us

  • About AJNR
  • Editorial Board
  • Editorial Board Alumni
  • Alerts
  • Permissions
  • Not an AJNR Subscriber? Join Now
  • Advertise with Us
  • Librarian Resources
  • Feedback
  • Terms and Conditions
  • AJNR Editorial Board Alumni

American Society of Neuroradiology

  • Not an ASNR Member? Join Now

© 2025 by the American Society of Neuroradiology All rights, including for text and data mining, AI training, and similar technologies, are reserved.
Print ISSN: 0195-6108 Online ISSN: 1936-959X

Powered by HighWire