Skip to main content
Advertisement

Main menu

  • Home
  • Content
    • Current Issue
    • Accepted Manuscripts
    • Article Preview
    • Past Issue Archive
    • AJNR Case Collection
    • Case of the Week Archive
    • Classic Case Archive
    • Case of the Month Archive
  • Special Collections
    • Spinal CSF Leak Articles (Jan 2020-June 2024)
    • 2024 AJNR Journal Awards
    • Most Impactful AJNR Articles
  • Multimedia
    • AJNR Podcast
    • AJNR Scantastics
    • Video Articles
  • For Authors
    • Submit a Manuscript
    • Author Policies
    • Fast publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Manuscript Submission Guidelines
    • Imaging Protocol Submission
    • Submit a Case for the Case Collection
  • About Us
    • About AJNR
    • Editorial Board
  • More
    • Become a Reviewer/Academy of Reviewers
    • Subscribers
    • Permissions
    • Alerts
    • Feedback
    • Advertisers
    • ASNR Home
  • Other Publications
    • ajnr

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
    • AJNR Case Collection
    • Case of the Week Archive
    • Classic Case Archive
    • Case of the Month Archive
  • Special Collections
    • Spinal CSF Leak Articles (Jan 2020-June 2024)
    • 2024 AJNR Journal Awards
    • Most Impactful AJNR Articles
  • Multimedia
    • AJNR Podcast
    • AJNR Scantastics
    • Video Articles
  • For Authors
    • Submit a Manuscript
    • Author Policies
    • Fast publishing of Accepted Manuscripts
    • Graphical Abstract Preparation
    • Manuscript Submission Guidelines
    • Imaging Protocol Submission
    • Submit a Case for the Case Collection
  • About Us
    • About AJNR
    • Editorial Board
  • 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

Welcome to the new AJNR, Updated Hall of Fame, and more. Read the full announcements.


AJNR is seeking candidates for the position of Associate Section Editor, AJNR Case Collection. Read the full announcement.

 

Research ArticleHead & Neck

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 (16)
  • Google Scholar

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

  • Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging
    Noriyuki Fujima, Koji Kamagata, Daiju Ueda, Shohei Fujita, Yasutaka Fushimi, Masahiro Yanagawa, Rintaro Ito, Takahiro Tsuboyama, Mariko Kawamura, Takeshi Nakaura, Akira Yamada, Taiki Nozaki, Tomoyuki Fujioka, Yusuke Matsui, Kenji Hirata, Fuminari Tatsugami, Shinji Naganawa
    Magnetic Resonance in Medical Sciences 2023 22 4
  • Machine Learning for Medical Image Translation: A Systematic Review
    Jake McNaughton, Justin Fernandez, Samantha Holdsworth, Benjamin Chong, Vickie Shim, Alan Wang
    Bioengineering 2023 10 9
  • Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning
    Mohamed A. Bahloul, Saima Jabeen, Sara Benoumhani, Habib Abdulmohsen Alsaleh, Zehor Belkhatir, Areej Al‐Wabil
    Journal of Applied Clinical Medical Physics 2024 25 11
  • Bone injury imaging in knee and ankle joints using fast-field-echo resembling a CT using restricted echo-spacing MRI: a feasibility study
    Nan Wang, Zhengshi Jin, Funing Liu, Lihua Chen, Ying Zhao, Liangjie Lin, Ailian Liu, Qingwei Song
    Frontiers in Endocrinology 2024 15
  • Deep Learning Techniques and Imaging in Otorhinolaryngology—A State-of-the-Art Review
    Christos Tsilivigkos, Michail Athanasopoulos, Riccardo di Micco, Aris Giotakis, Nicholas S. Mastronikolis, Francesk Mulita, Georgios-Ioannis Verras, Ioannis Maroulis, Evangelos Giotakis
    Journal of Clinical Medicine 2023 12 22
  • Evaluating the Hounsfield unit assignment and dose differences between CT‐based standard and deep learning‐based synthetic CT images for MRI‐only radiation therapy of the head and neck
    Kamal Singhrao, Catherine Lu Dugan, Christina Calvin, Luis Pelayo, Sue Sun Yom, Jason Wing‐Hong Chan, Jessica Elizabeth Scholey, Lisa Singer
    Journal of Applied Clinical Medical Physics 2024 25 1
  • Utility of zero echo time (ZTE) sequence for assessing bony lesions of skull base and calvarium
    V. Chauhan, K. Harikishore, S. Girdhar, S. Kaushik, F. Wiesinger, C. Cozzini, M. Carl, M. Fung, B.B. Mehta, B. Thomas, C. Kesavadas
    Clinical Radiology 2024 79 12
  • A systematic review of deep learning techniques for generating synthetic CT images from MRI data
    Isaac Kwesi Acquah, Shiraz Issahaku, Samuel Nii Adu Tagoe
    Polish Journal of Medical Physics and Engineering 2025 31 1
  • Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers
    Jacob Antunes, Tony Young, Dane Pittock, Paul Jacobs, Aaron Nelson, Jon Piper, Shrikant Deshpande
    Radiotherapy and Oncology 2025 205
  • Synthetic CT generation using Zero TE MR for head-and-neck radiotherapy
    Iris Lauwers, Marta Capala, Sandeep Kaushik, László Ruskó, Cristina Cozzini, Jean-Paul Kleijnen, Jonathan Wyatt, Hazel McCallum, Gerda Verduijn, Florian Wiesinger, Juan Hernandez-Tamames, Steven Petit
    Radiotherapy and Oncology 2025 205

More in this TOC Section

Head & Neck

  • NI-RADS for HEAD&NECK Cancer Recurrence
  • WHO Classification Update: Nasal&Skull Base Tumors
  • Peritumoral Signal in Vestibular Schwannomas
Show more Head & Neck

Functional

  • Glutaric Aciduria Type 1: DK vs. Conventional MRI
  • Kurtosis and Epileptogenic Tubers: A Pilot Study
  • Choroid Plexus Calcification&Microglial Activation
Show more Functional

Similar Articles

Advertisement

Indexed Content

  • Current Issue
  • Accepted Manuscripts
  • Article Preview
  • Past Issues
  • Editorials
  • Editors Choice
  • Fellow Journal Club
  • Letters to the Editor

Cases

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

Special Collections

  • Special Collections

Resources

  • News and Updates
  • Turn around Times
  • Submit a Manuscript
  • Author Policies
  • Manuscript Submission Guidelines
  • Evidence-Based Medicine Level Guide
  • Publishing Checklists
  • Graphical Abstract Preparation
  • Imaging Protocol Submission
  • Submit a Case
  • Become a Reviewer/Academy of Reviewers
  • Get Peer Review Credit from Publons

Multimedia

  • AJNR Podcast
  • AJNR SCANtastic
  • Video Articles

About Us

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

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