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

Research ArticleHead and Neck Imaging

Deep Learning for Synthetic CT from Bone MRI in the Head and Neck

S. Bambach and M.-L. Ho
American Journal of Neuroradiology August 2022, 43 (8) 1172-1179; DOI: https://doi.org/10.3174/ajnr.A7588
S. Bambach
aFrom the Abigail Wexner Research Institute at Nationwide Children’s Hospital (S.B.), Columbus, Ohio
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for S. Bambach
M.-L. Ho
bDepartment of Radiology (M.-L.H.), Nationwide Children’s Hospital, Columbus, Ohio.
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for M.-L. Ho
  • Article
  • Figures & Data
  • Info & Metrics
  • Responses
  • References
  • PDF
Loading

References

  1. 1.↵
    1. Du J,
    2. Hermida JC,
    3. Diaz E, et al
    . Assessment of cortical bone with clinical and ultrashort echo time sequences. Magn Reson Med 2013;70:697–704 doi:10.1002/mrm.24497 pmid:23001864
    CrossRefPubMed
  2. 2.↵
    1. Schieban K,
    2. Weiger M,
    3. Hennel F, et al
    . ZTE imaging with enhanced flip angle using modulated excitation. Magn Reson Med 2015;74:684–93 doi:10.1002/mrm.25464 pmid:25242318
    CrossRefPubMed
  3. 3.↵
    1. Eley KA,
    2. McIntyre AG,
    3. Watt-Smith SR, et al
    . “Black bone” MRI: a partial flip angle technique for radiation reduction in craniofacial imaging. Br J Radiol 2012;85:272–78 doi:10.1259/bjr/95110289 pmid:22391497
    Abstract/FREE Full Text
  4. 4.↵
    1. Tiberi G,
    2. Costagli M,
    3. Biagi L, et al
    . SAR prediction in adults and children by combining measured B1+ maps and simulations at 7.0 Tesla. J Magn Reson Imaging 2016;44:1048–55 doi:10.1002/jmri.25241 pmid:27042956
    CrossRefPubMed
  5. 5.↵
    1. Alibek S,
    2. Vogel M,
    3. Sun W, et al
    . Acoustic noise reduction in MRI using Silent Scan: an initial experience. Diagn Interv Radiol 2014;20:360–63 doi:10.5152/dir.2014.13458 pmid:24808439
    CrossRefPubMed
  6. 6.↵
    1. Eley KA,
    2. Watt-Smith SR,
    3. Golding SJ
    . “Black bone” MRI: a potential alternative to CT when imaging the head and neck: report of eight clinical cases and review of the Oxford experience. Br J Radiol 2012;85:1457–64 doi:10.1259/bjr/16830245 pmid:23091288
    Abstract/FREE Full Text
  7. 7.↵
    1. Lu A,
    2. Gorny KC,
    3. Ho ML
    . Zero TE MRI for craniofacial bone imaging. AJNR Am J Neuroradiol 2019;40:1562–66 doi:10.3174/ajnr.A6175 pmid:31467238
    Abstract/FREE Full Text
  8. 8.↵
    1. Cho SB,
    2. Baek HJ,
    3. Ryu KH, et al
    . Clinical feasibility of zero TE skull MRI in patients with head trauma in comparison with CT: a single-center study. AJNR Am J Neuroradiol 2019;40:109–15 doi:10.3174/ajnr.A5916 pmid:30545839
    Abstract/FREE Full Text
  9. 9.↵
    1. Hsu SH,
    2. Cao Y,
    3. Lawrence TS, et al
    . Quantitative characterizations of ultrashort echo (UTE) images for supporting air-bone separation in the head. Phys Med Biol 2015;60:2869–80 doi:10.1088/0031-9155/60/7/2869 pmid:25776205
    CrossRefPubMed
  10. 10.↵
    1. Ghose S,
    2. Dowling JA,
    3. Rai R, et al
    . Substitute CT generation from a single ultra short time echo MRI sequence: preliminary study. Phys Med Biol 2017;62:2950–60 doi:10.1088/1361-6560/aa508a pmid:28306546
    CrossRefPubMed
  11. 11.↵
    1. Kraus KM,
    2. Jäkel O,
    3. Niebuhr NI, et al
    . Generation of synthetic CT data using patient specific daily MR image data and image registration. Phys Med Biol 2017;62:1358–77 doi:10.1088/1361-6560/aa5200 pmid:28114107
    CrossRefPubMed
  12. 12.↵
    1. Wiesinger F,
    2. Bylund M,
    3. 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;80:1440–51 doi:10.1002/mrm.27134 pmid:29457287
    CrossRefPubMed
  13. 13.↵
    1. Leynes AP,
    2. Yang J,
    3. Wiesinger F, et al
    . Zero-echo-time and Dixon deep pseudo-CT (ZeDD CT): direct generation of pseudo-CT images for pelvic PET/MRI attenuation correction using deep convolutional neural networks with multiparametric MRI. J Nucl Med 2018;59:852–58 doi:10.2967/jnumed.117.198051 pmid:29084824
    Abstract/FREE Full Text
  14. 14.
    1. Gong K,
    2. Yang J,
    3. 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 doi:10.1088/1361-6560/aac763 pmid:29790857
    CrossRefPubMed
  15. 15.
    1. Nie D,
    2. Cao X,
    3. Gao Y, et al
    . Estimating CT image from MRI data using 3D fully convolutional networks. Deep Learn Data Label Med Appl (2016) 2016;2016:170–78 doi:10.1007/978-3-319-46976-8_18 pmid:29075680
    CrossRefPubMed
  16. 16.
    1. Andreasen D,
    2. Van Leemput K,
    3. Hansen RH, et al
    . Patch-based generation of a pseudo CT from conventional MRI sequences for MRI-only radiotherapy of the brain. Med Phys 2015;42:1596–605 doi:10.1118/1.4914158 pmid:25832050
    CrossRefPubMed
  17. 17.↵
    1. Boukellouz W,
    2. Moussaoui A
    . Magnetic resonance-driven pseudo CT image using patch-based multi-modal feature extraction and ensemble learning with stacked generalization. Journal of King Saud University: Computer and Information Sciences 2021;33:999–1007
    CrossRef
  18. 18.↵
    1. Otsu N
    . A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 1979;9:62–66 doi:10.1109/TSMC.1979.4310076
    CrossRefPubMedWeb of Science
  19. 19.↵
    1. Ronneberger O,
    2. Fischer P,
    3. Brox T
    . U-net: convolutional networks for biomedical image segmentation: Medical Image Computing and Computer-Assisted Intervention (MICCAI). arXiv 1505.04597 [cs.CV] 2015 https://arxiv.org/abs/1505.04597. Accessed March 30, 2021
  20. 20.↵
    1. Simonyan K,
    2. Zisserman A
    . Very deep convolutional networks for large-scale image recognition. arXiv 1409.1556 2015. https://arxiv.org/abs/1409.1556v4. Accessed March 30, 2021
  21. 21.↵
    1. Deng J,
    2. Dong W,
    3. Socher R, et al
    . ImageNet: a large-scale hierarchical image database. In: Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami Beach, Florida. June 20–25, 2009
  22. 22.↵
    1. Kingma DP,
    2. Ba J
    . Adam: a method for stochastic optimization. arXiv 1412.6980 2017. https://arxiv.org/abs/1412.6980. Accessed March 30, 2021
  23. 23.↵
    1. Goodfellow I, et al
    . Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Quebec, Canada. December 8–13, 2014; 2672–80
  24. 24.
    1. Wolterink JM,
    2. Dinkla Am Savenije MH, et al
    . Deep MR to CT synthesis using unpaired data. Simulation and Synthesis in Medical Imaging. Lecture Notes in Computer Science. arXiv 1708.01155 [cs.CV] 2017. https://arxiv.org/abs/1708.01155. Accessed March 30, 2021
  25. 25.
    1. Zhu JY,
    2. Park T,
    3. Isola P, et al
    . Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy. October 22–29, 2017 doi:10.1109/ICCV.2017.244
    CrossRef
  26. 26.↵
    1. Isola P,
    2. Zhu JY,
    3. Zhou T, et al
    . Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii. July 21–26, 2017 doi:10.1109/CVPR.2017.632
    CrossRef
  27. 27.↵
    1. Li W,
    2. Li Y,
    3. Qin W, et al
    . Magnetic resonance image (MRI) synthesis from brain computed tomography (CT) images based on deep learning methods for magnetic resonance (MR)-guided radiotherapy. Quan Imaging Med Surg 2020;10:1223–36 doi:10.21037/qims-19-885 pmid:32550132
    CrossRefPubMed
  28. 28.↵
    1. Kornblith S,
    2. Shlens J,
    3. Le QV
    . Do better imagenet models transfer better? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, California. June 15–20, 2019 doi:10.1109/CVPR.2019.00277
    CrossRef
  29. 29.
    1. Raghu M,
    2. Zhang C,
    3. Kleinberg J, et al
    . Transfusion: understanding transfer learning for medical imaging. arXiv 2019. https://arxiv.org/abs/1902.07208. Accessed March 30, 2021
  30. 30.↵
    1. Anwar SM,
    2. Majid M,
    3. Qayyum A, et al
    . Medical image analysis using convolutional neural networks: a review. J Med Sys 2018;42: 226 doi:10.1007/s10916-018-1088-1 pmid:30298337
    CrossRefPubMed
  31. 31.↵
    1. Boulanger M,
    2. Nunes JC,
    3. Chourak H, et al
    . Deep learning methods to generate synthetic CT from MRI in radiotherapy: a literature review. Phys Med 2021;89:265–81 doi:10.1016/j.ejmp.2021.07.027 pmid:34474325
    CrossRefPubMed
  32. 32.↵
    1. Spadea MF,
    2. Maspero M,
    3. Zaffino P, et al
    . Deep learning based synthetic-CT generation in radiotherapy and PET: a review. Med Phys 2021;48:6537–66 doi:10.1002/mp.15150 pmid:34407209
    CrossRefPubMed
  33. 33.↵
    1. Bambach S,
    2. Ho ML
    . Bone MRI: can it replace CT: 2nd AI Award. In: Proceedings of the American Society of Functional Neuroradiology, Artificial Intelligence Workshop, February 5, 2021
  34. 34.
    1. Smith M,
    2. Bambach S,
    3. Selvaraj B, et al
    . Zero-TE MRI: potential applications in the oral cavity and oropharynx. Top Magn Reson Imaging 2021;30: 105–15 doi:10.1097/RMR.0000000000000279 pmid:33828062
    CrossRefPubMed
  35. 35.
    1. Kobayashi N,
    2. Bambach S,
    3. Ho ML
    . Ultrashort echo-time MR imaging of the pediatric head and neck. Magn Reson Imaging Clin N Am 2021;29:583–93 doi:10.1016/j.mric.2021.06.008 pmid:34717846
    CrossRefPubMed
  36. 36.↵
    1. Wiesinger F,
    2. Ho ML
    . Zero-TE MRI: principles and applications in the head and neck. Br J Radiol 2022 June 10. [Epub ahead of print]
  37. 37.↵
    1. Aouadi S,
    2. Vasic A,
    3. Paloor S, et al
    . Generation of synthetic CT using multi-scale and dual-contrast patches for brain MRI-only external beam radiotherapy. Phys Med 2017;42:174–84 doi:10.1016/j.ejmp.2017.09.132 pmid:29173912
    CrossRefPubMed
  38. 38.
    1. Dinkla AM,
    2. Florkow MC,
    3. Maspero M, et al
    . Dosimetric evaluation of synthetic CT for head and neck radiotherapy generated by a patch-based three-dimensional convolutional neural network. Med Phys 2019;46:4095–104 doi:10.1002/mp.13663 pmid:31206701
    CrossRefPubMed
  39. 39.
    1. Roy S,
    2. Carass A,
    3. Jog A, et al
    . MR to CT registration of brains using image synthesis. Proc SPIE Int Soc Opt Eng 2014;9034 doi:10.1117/12.2043954] pmid:25057341
    CrossRefPubMed
  40. 40.
    1. Lee J,
    2. Carass A,
    3. Jog A, et al
    . Multi-atlas-based CT synthesis from conventional MRI with patch-based refinement for MRI-based radiotherapy planning. Proc SPIE Int Soc Opt Eng 2017;10133:1013311 doi:10.1117/12.2254571 pmid:29142336
    CrossRefPubMed
  41. 41.↵
    1. Klages P,
    2. Benslimane I,
    3. Riyahi S, et al
    . Patch-based generative adversarial neural network models for head and neck MR-only planning. Med Phys 2020;47:626–42 doi:10.1002/mp.13927 pmid:31733164
    CrossRefPubMed
PreviousNext
Back to top

In this issue

American Journal of Neuroradiology: 43 (8)
American Journal of Neuroradiology
Vol. 43, Issue 8
1 Aug 2022
  • 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.
Deep Learning for Synthetic CT from Bone MRI in the Head and Neck
(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
S. Bambach, M.-L. Ho
Deep Learning for Synthetic CT from Bone MRI in the Head and Neck
American Journal of Neuroradiology Aug 2022, 43 (8) 1172-1179; DOI: 10.3174/ajnr.A7588

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
Deep Learning for Synthetic CT from Bone MRI
S. Bambach, M.-L. Ho
American Journal of Neuroradiology Aug 2022, 43 (8) 1172-1179; DOI: 10.3174/ajnr.A7588
del.icio.us logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Purchase

Jump to section

  • Article
    • Abstract
    • ABBREVIATIONS:
    • MATERIALS AND METHODS
    • RESULTS
    • DISCUSSION
    • CONCLUSIONS
    • Acknowledgments
    • 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
  • Google Scholar

This article has not yet been cited by articles in journals that are participating in Crossref Cited-by Linking.

More in this TOC Section

Head and Neck Imaging

  • Hydrops Herniation into the Semicircular Canals
  • ASL Sensitivity for Head and Neck Paraganglioma
  • Post SRS Peritumoral Hyperintense Signal of VSs
Show more Head and Neck Imaging

Functional

  • Kurtosis and Epileptogenic Tubers: A Pilot Study
  • Glutaric Aciduria Type 1: DK vs. Conventional MRI
  • Multiparametric MRI in PEDS Pontine Glioma
Show more Functional

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
  • Book Reviews

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