Skip to main content
SpringerLink
Log in

Generating Artificial Artifacts for Motion Artifact Detection in Chest CT

  • Conference paper
  • First Online:
Simulation and Synthesis in Medical Imaging (SASHIMI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13570))

Included in the following conference series:

  • 688 Accesses

Abstract

Motion artifacts can have a detrimental effect on the analysis of chest CT scans, because the artifacts can mimic or obscure genuine pathological features. Localising motion artifacts in the lungs can improve diagnosis quality. The diverse appearance of artifacts requires large quantities of annotations to train a detection model, but manual annotations can be subjective, unreliable, and are labour intensive to obtain. We propose a novel method (Code is available at https://github.com/guusvanderham/artificial-motion-artifacts-for-ct) for generating artificial motion artifacts in chest CT images, based on simulated CT reconstruction. We use these artificial artifacts to train fully convolutional networks that can detect real motion artifacts in chest CT scans. We evaluate our method on scans from the public LIDC, RIDER and COVID19-CT datasets and find that it is possible to train detection models with artificially generated artifacts. Generated artifacts greatly improve performance when the availability of manually annotated scans is limited.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Armato, S.G., III., McLennan, G., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915–931 (2011)

    Article  Google Scholar 

  2. Barrett, J.F., Keat, N.: Artifacts in CT: recognition and avoidance. Radiographics 24(6), 1679–1691 (2004)

    Article  Google Scholar 

  3. Beri, P.: Detection of motion artifacts in thoracic CT scans (2020)

    Google Scholar 

  4. Bian, Z., Charbonnier, J.P., Liu, J., Zhao, D., Lynch, D.A., van Ginneken, B.: Small airway segmentation in thoracic computed tomography scans: a machine learning approach. Phys. Med. Biol. 63(15), 155024 (2018)

    Article  Google Scholar 

  5. Cohen, J.P., Luck, M., Honari, S.: Distribution matching losses can hallucinate features in medical image translation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 529–536. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_60

    Chapter  Google Scholar 

  6. Elss, T., Nickisch, H., Wissel, T., Bippus, R., Morlock, M., Grass, M.: Motion estimation in coronary CT angiography images using convolutional neural networks. MIDL (2018)

    Google Scholar 

  7. Elss, T., et al.: Deep-learning-based CT motion artifact recognition in coronary arteries. In: Medical Imaging 2018: Image Processing, vol. 10574, p. 1057416 (2018)

    Google Scholar 

  8. Fleiss, J.L., Levin, B., Paik, M.C.: Statistical Methods for Rates and Proportions. Wiley, Hoboken (2013)

    MATH  Google Scholar 

  9. Jiang, C., et al.: Wasserstein generative adversarial networks for motion artifact removal in dental CT imaging. In: Medical Imaging 2019: Physics of Medical Imaging, vol. 10948, p. 1094836. International Society for Optics and Photonics (2019)

    Google Scholar 

  10. Kim, D., et al.: Motion correction for routine X-ray lung CT imaging. Sci. Rep. 11(1), 1–10 (2021)

    Article  Google Scholar 

  11. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640651 (2014)

    Google Scholar 

  12. Lossau, T., et al.: Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks. Med. Image Anal. 52, 68–79 (2019)

    Article  Google Scholar 

  13. Persson, G.F., et al.: Artifacts in conventional computed tomography (CT) and free breathing four-dimensional CT induce uncertainty in gross tumor volume determination. Int. J. Radiat. Oncol. Biol. Phys. 80(5), 1573–1580 (2011)

    Article  Google Scholar 

  14. Rad, Z.I., Peyvandi, R.G., Heshmati, R.: Motion detection in CT images with a novel fast technique. Instrum. Exp. Tech. 56(3), 276–282 (2013)

    Article  Google Scholar 

  15. Rohkohl, C., Bruder, H., Stierstorfer, K., Flohr, T.: Improving best-phase image quality in cardiac CT by motion correction with mam optimization. Med. Phys. 40(3), 031901 (2013)

    Article  Google Scholar 

  16. Schofield, R., et al.: Image reconstruction: part 1-understanding filtered back projection, noise and image acquisition. J. Cardiovasc. Comput. Tomogr. 14(3), 219–225 (2020)

    Article  Google Scholar 

  17. Zhao, B., Schwartz, L.H., Kris, M.G.: Data From RIDER Lung CT. The Cancer Imaging Archive (2015). https://doi.org/10.7937/K9/TCIA.2015.U1X8A5NR

  18. Shakouri, S., Bakhshali, M.A., Layegh, P., et al.: Covid19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed Covid-19 diagnosis. BMC Res. Notes 14(1), 1–3 (2021)

    Article  Google Scholar 

  19. Su, K., Zhou, E., Sun, X., Wang, C., Yu, D., Luo, X.: Pre-trained StyleGAN based data augmentation for small sample brain CT motion artifacts detection. In: Yang, X., Wang, C.-D., Islam, M.S., Zhang, Z. (eds.) ADMA 2020. LNCS (LNAI), vol. 12447, pp. 339–346. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65390-3_26

    Chapter  Google Scholar 

  20. Sun, X., et al.: Motion artifacts detection from computed tomography images. In: Yang, X., Wang, C.-D., Islam, M.S., Zhang, Z. (eds.) ADMA 2020. LNCS (LNAI), vol. 12447, pp. 347–359. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65390-3_27

    Chapter  Google Scholar 

  21. Van Aarle, W., et al.: The ASTRA toolbox: a platform for advanced algorithm development in electron tomography. Ultramicroscopy 157, 35–47 (2015)

    Article  Google Scholar 

  22. Van Stevendaal, U., Von Berg, J., Lorenz, C., Grass, M.: A motion-compensated scheme for helical cone-beam reconstruction in cardiac CT angiography. Med. Phys. 35(7Part1), 3239–3251 (2008)

    Google Scholar 

  23. Van Tulder, G.: elasticdeform: elastic deformations for n-dimensional images (2021). https://doi.org/10.5281/zenodo.4569691. Accessed 01 Mar 2022

  24. Wang, C., Sun, X., Zhang, B., Lai, G., Yu, D., Su, K.: Brain CT image with motion artifact augmentation based on PGGAN and FBP for artifact detection. In: Yang, X., Wang, C.-D., Islam, M.S., Zhang, Z. (eds.) ADMA 2020. LNCS (LNAI), vol. 12447, pp. 370–378. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65390-3_29

    Chapter  Google Scholar 

Download references

Acknowledgments

G. van Tulder was financially supported by EFRO/OP-Oost (PROJ-00887).

Author information

Authors and Affiliations

  1. Radboud University, Nijmegen, The Netherlands

    Guus van der Ham & Gijs van Tulder

  2. Thirona, Nijmegen, The Netherlands

    Guus van der Ham, Rudolfs Latisenko & Michail Tsiaousis

Authors
  1. Guus van der Ham
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rudolfs Latisenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michail Tsiaousis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gijs van Tulder
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gijs van Tulder .

Editor information

Editors and Affiliations

  1. NVIDIA, Santa Clara, CA, USA

    Can Zhao

  2. Masaryk University, Brno, Czech Republic

    David Svoboda

  3. University of Twente, Enschede, The Netherlands

    Jelmer M. Wolterink

  4. Universidad de Los Andes, Bogotá, Colombia

    Maria Escobar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

van der Ham, G., Latisenko, R., Tsiaousis, M., van Tulder, G. (2022). Generating Artificial Artifacts for Motion Artifact Detection in Chest CT. In: Zhao, C., Svoboda, D., Wolterink, J.M., Escobar, M. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2022. Lecture Notes in Computer Science, vol 13570. Springer, Cham. https://doi.org/10.1007/978-3-031-16980-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16980-9_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16979-3

  • Online ISBN: 978-3-031-16980-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation