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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Barrett, J.F., Keat, N.: Artifacts in CT: recognition and avoidance. Radiographics 24(6), 1679–1691 (2004)
Beri, P.: Detection of motion artifacts in thoracic CT scans (2020)
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)
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
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)
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)
Fleiss, J.L., Levin, B., Paik, M.C.: Statistical Methods for Rates and Proportions. Wiley, Hoboken (2013)
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)
Kim, D., et al.: Motion correction for routine X-ray lung CT imaging. Sci. Rep. 11(1), 1–10 (2021)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640651 (2014)
Lossau, T., et al.: Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks. Med. Image Anal. 52, 68–79 (2019)
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)
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)
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)
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)
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
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)
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
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
Van Aarle, W., et al.: The ASTRA toolbox: a platform for advanced algorithm development in electron tomography. Ultramicroscopy 157, 35–47 (2015)
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)
Van Tulder, G.: elasticdeform: elastic deformations for n-dimensional images (2021). https://doi.org/10.5281/zenodo.4569691. Accessed 01 Mar 2022
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
Acknowledgments
G. van Tulder was financially supported by EFRO/OP-Oost (PROJ-00887).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
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)
