Abstract
In internal radiation therapy, dosimetry is essential to predict its efficacy and potential side effects. Contrast enhanced computed tomography (ceCT) is most commonly used as starting point for planning. However, native CT (nCT) is required for accurate dosimetry computations. In thiswork,we propose an in-silico method to remove the contrast agent from ceCT images so that the Hounsfield Units (HU) would be similar to those in nCT. Two approaches, one paired-image neural network (NN) and one un-paired NN, were applied to ceCT/nCT image pairs for contrast removal.We evaluated their performance in terms of HU values, and performed dosimetry calculations on the original nCT and ceCT, and on the in-silico nCTs to evaluate the impact on the dose rate. The two approaches yielded good results both in terms of HU reduction (more than 30%) and in the difference of dose rate against the original nCT (less than 1.38% vs. 4.76%).
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
Preview
Unable to display preview. Download preview PDF.
References
Liugang G, Kai X, Chunying L, Zhengda L, Jianfeng S, Tao L et al. Generation of virtual non-contrast CT from intravenous enhanced CT in radiotherapy using convolutional neural networks. Front Oncol. 2020;10.
Jakubicek R, Vicar T, Chmelik J, Ourednicek P, Jan J. Deep-learning based prediction of virtual non-contrast CT images. 2021 International Symposium on Electrical, Electronics and Information Engineering. Seoul (Republic of Korea), 2021.
Song C, He B, Chen H, Jia S, Chen X, Jia F. Non-contrast CT liver segmentation using CycleGAN data augmentation from contrast enhanced CT. Interpretable and Annotation-Efficient Learning for Medical Image Computing. Ed. by Cardoso J, Van Nguyen H, Heller N, Henriques Abreu P, Isgum I, Silva W et al. (Lecture Notes in Computer Science). Cham, 2020.
Sandfort V, Yan K, Pickhardt PJ, Summers RM. Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks. Sci Rep. 2019;9(1).
Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S et al. nnU-Net: self-adapting Framework for U-Net-Based Medical Image Segmentation. arXiv:1809.10486 [cs]. 2018.
Falk T, Mai D, Bensch R, Çiçek Ö, Abdulkadir A, Marrakchi Y et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods. 2019;16(1).
Chen L, Bentley P, Mori K, Misawa K, Fujiwara M, Rueckert D. Self-supervised learning for medical image analysis using image context restoration. Med Image Anal. 2019;58.
Zhu JY, Park T, Isola P, Efros AA. Unpaired image-to-image translation using cycleconsistent adversarial networks. IEEE International Conference on Computer Vision. 2017.
Papadimitroulas P. Dosimetry applications in GATE Monte Carlo toolkit. Phys Medica. 2017;41.
Schneider W, Bortfeld T, Schlegel W. Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. Phys Med Biol. 2000;45(2).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Ganß, M. et al. (2023). Deep Learning Approaches for Contrast Removal from Contrast-enhanced CT. In: Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2023. BVM 2023. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-41657-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-658-41657-7_18
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-41656-0
Online ISBN: 978-3-658-41657-7
eBook Packages: Computer Science and Engineering (German Language)