Abstract
By injecting contrast agent during a CT acquisition, the vascular system can be enhanced. This acquisition type is known as CT Angiography (CTA). However, due to typically lower dose levels of CTA scans compared to non-contrast CT acquisitions (NCCT) and the employed reconstruction designed specifically for vessel reconstruction, soft tissue contrast in the brain parenchyma is usually subpar. Hence, an NCCT scan is preferred for the visualization of such tissue. We propose SyNCCT, an approach which synthesizes NCCT images from the CTA domain by removing enhanced vessel structures and improving soft tissue contrast. Contrary to virtual non-contrast (VNC) images based on dual energy scans, which target the physically accurate removal of iodine rather than generating a realistic NCCT with improved gray/white matter separation, our approach only requires a conventional single-energy acquisition. By design, our method integrates prior domain knowledge and employs residual learning as well as a discriminator to achieve perceptual realism. In our data set of patients with ischemic stroke, the absolute differences in automatic ASPECT scoring, which rates early signs of an occlusion in the anterior circulation on a scale from 0 (most severe) to 10 (no signs), was 0.78 ± 0.75 (median of 1) when comparing our SyNCCT to the real NCCT images. Qualitatively, realistic appearance of the images was confirmed by means of a Turing test with a radiologist, who classified 64% of 64 (32 real, 32 generated) images correctly. Two other physicians classified 65% correctly, on average.
Keywords
- Computed tomography angiography
- Domain transfer
- Single energy CT
This is a preview of subscription content, access via your institution.
Buying options
References
Barber, P.A., Demchuk, A.M., Zhang, J., Buchan, A.M., Group, A.S., et al.: Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy. The Lancet 355(9216), 1670–1674 (2000)
Bibb, R., Eggbeer, D., Paterson, A.: Medical Modelling: The Application of Advanced Design and Rapid Prototyping Techniques in Medicine. Woodhead Publishing (2014)
Bône, A., et al.: Contrast-enhanced brain MRI synthesis with deep learning: key input modalities and asymptotic performance. In: 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1159–1163. IEEE (2021)
Goodfellow, I.J., et al.: Generative adversarial networks. arXiv preprint arXiv:1406.2661 (2014)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Klimont, M., et al.: Deep learning for cerebral angiography segmentation from non-contrast computed tomography. PLoS ONE 15(7), 1–15 (2020). https://doi.org/10.1371/journal.pone.0237092
Lee, D., Kim, H., Choi, B., Kim, H.J.: Development of a deep neural network for generating synthetic dual-energy chest x-ray images with single x-ray exposure. Phys. Med. Biol. 64(11), 115017 (2019)
Lev, M., Gonzalez, R.: 17 - CT angiography and CT perfusion imaging. In: Toga, A.W., Mazziotta, J.C. (eds.) Brain Mapping: The Methods, 2nd edn, pp. 427–484. Academic Press, San Diego (2002). https://doi.org/10.1016/B978-012693019-1/50019-8, https://www.sciencedirect.com/science/article/pii/B9780126930191500198
Li, L., et al.: Comparison of the performance between frontier aspects software and different levels of radiologists on assessing CT examinations of acute ischaemic stroke patients. Clin. Radiol. 75(5), 358–365 (2020)
Lyu, T., et al.: Estimating dual-energy CT imaging from single-energy CT data with material decomposition convolutional neural network. Med. Image Anal. 70, 102001 (2021)
Maegerlein, C., et al.: Automated calculation of the alberta stroke program early CT score: feasibility and reliability. Radiology 291(1), 141–148 (2019)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. arXiv preprint arXiv:1912.01703 (2019)
Poirot, M.G., et al.: Physics-informed deep learning for dual-energy computed tomography image processing. Sci. Rep. 9(1), 1–9 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Thamm, F., Jürgens, M., Ditt, H., Maier, A.: VirtualDSA++: automated segmentation, vessel labeling, occlusion detection and graph search on CT-angiography data. In: Kozlíková, B., Krone, M., Smit, N., Nieselt, K., Raidou, R.G. (eds.) Eurographics Workshop on Visual Computing for Biology and Medicine. The Eurographics Association (2020). https://doi.org/10.2312/vcbm.20201181
Toepker, M., et al.: Virtual non-contrast in second-generation, dual-energy computed tomography: reliability of attenuation values. Eur. J. Radiol. 81(3), e398–e405 (2012)
Wang, Z., Bovik, A.C.: A universal image quality index. IEEE Signal Process. Lett. 9(3), 81–84 (2002)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Thamm, F., Taubmann, O., Denzinger, F., Jürgens, M., Ditt, H., Maier, A. (2021). SyNCCT: Synthetic Non-contrast Images of the Brain from Single-Energy Computed Tomography Angiography. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_64
Download citation
DOI: https://doi.org/10.1007/978-3-030-87234-2_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87233-5
Online ISBN: 978-3-030-87234-2
eBook Packages: Computer ScienceComputer Science (R0)
-
Published in cooperation with
http://miccai.org/
