Abstract
Various harmonization methods have been employed for obtaining MRI from different scanners. However, no study has yet focused on the clinical utility of the CycleGAN technique in reducing MRI interscanner variability for patients with brain metastasis across longitudinal visits. We developed a head-to-head and longitudinal CycleGAN-based deep learning (DL) algorithm for MRI harmonization and validated its utility for follow-up (FU) MRI evaluation in patients with unchanged brain metastasis, who had FU MRI taken using a different MRI scanner. We trained the head-to-head and longitudinal CycleGAN to generate harmonized second postcontrast 3D T1W MR images with similar image impressions as the initial postcontrast 3D T1W MR images. The image similarity scores between the baseline (BL) and harmonized FU images were higher than those between the baseline and original FU images. As compared with baseline, differences in the CNRs of brain subregions were lower for the harmonized FU images than for the original FU images. More cases were read to be unchanged on the harmonized FU images than on the original FU images in terms of border, size, and contrast enhancement at a higher level of diagnostic confidence. The proposed CycleGAN algorithm may potentially decrease false positivity for the diagnosis of progression in FU MRI evaluation of brain metastasis.
Similar content being viewed by others
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.
Code availability
The code for the proposed method is publicly available at https://github.com/Iceberg6618/CycleGAN-Harmonization. Detailed instructions, including installation, dataset preparation, and example usage, are provided in the repository’s README file. Users can directly access and run the code to reproduce the experiments and evaluate the method.
References
Kim, T. et al. Epidemiology of Intracranial Metastases in Korea: A National Cohort Investigation. Cancer Res. Treat. 50, 164–174. https://doi.org/10.4143/crt.2017.072 (2018).
Hatiboglu, M. A., Akdur, K. & Sawaya, R. Neurosurgical management of patients with brain metastasis. Neurosurg. Rev. 43, 483–495. https://doi.org/10.1007/s10143-018-1013-6 (2020).
Zuo, L. et al. Unsupervised MR harmonization by learning disentangled representations using information bottleneck theory. Neuroimage 243, 118569. https://doi.org/10.1016/j.neuroimage.2021.118569 (2021).
Stamoulou, E. et al. Harmonization Strategies in Multicenter MRI-Based Radiomics. J. Imaging. 8 https://doi.org/10.3390/jimaging8110303 (2022).
Hu, F. et al. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 274, 120125. https://doi.org/10.1016/j.neuroimage.2023.120125 (2023).
Abbasi, S. et al. Deep learning for the harmonization of structural MRI scans: a survey. Biomed. Eng. Online. 23 https://doi.org/10.1186/s12938-024-01280-6 (2024).
Fu, X. Digital Image Art Style Transfer Algorithm Based on CycleGAN. Comput. Intell. Neurosci. 2022(6075398). https://doi.org/10.1155/2022/6075398 (2022).
Kang, S. K. et al. Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy. Biomed. Eng. Lett. 11, 263–271. https://doi.org/10.1007/s13534-021-00195-8 (2021).
Kalantar, R. et al. CT-Based Pelvic T(1)-Weighted MR Image Synthesis Using UNet, UNet + + and Cycle-Consistent Generative Adversarial Network (Cycle-GAN). Front. Oncol. 11, 665807. https://doi.org/10.3389/fonc.2021.665807 (2021).
Kawahara, D. & Nagata, Y. T1-weighted and T2-weighted MRI image synthesis with convolutional generative adversarial networks. Rep. Pract. Oncol. Radiother. 26, 35–42. https://doi.org/10.5603/RPOR.a2021.0005 (2021).
Gebre, R. K. et al. Cross-scanner harmonization methods for structural MRI may need further work: A comparison study. Neuroimage 269, 119912. https://doi.org/10.1016/j.neuroimage.2023.119912 (2023).
Dikici, E. et al. Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI. IEEE J. Biomed. Health Inf. 24, 2883–2893. https://doi.org/10.1109/JBHI.2020.2982103 (2020).
Kaufmann, T. J. et al. Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases. Neuro Oncol. 22, 757–772. https://doi.org/10.1093/neuonc/noaa030 (2020).
Nyul, L. G., Udupa, J. K. & Zhang, X. New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging. 19, 143–150. https://doi.org/10.1109/42.836373 (2000).
Fortin, J. P. et al. Removing inter-subject technical variability in magnetic resonance imaging studies. Neuroimage 132, 198–212. https://doi.org/10.1016/j.neuroimage.2016.02.036 (2016).
Tian, D. et al. A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset. Neuroimage 257, 119297. https://doi.org/10.1016/j.neuroimage.2022.119297 (2022).
Zuo, L. et al. Information-based disentangled representation learning for unsupervised MR harmonization. in Information Processing in Medical Imaging. (eds Aasa Feragen et al.) 346–359 (Springer International Publishing).
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 2223–2232.
Zhang, H., Li, H., Dillman, J. R., Parikh, N. A. & He, L. Multi-contrast MRI image synthesis using switchable cycle-consistent generative adversarial networks. Diagnostics (Basel). https://doi.org/10.3390/diagnostics12040816 (2022).
Taigman, Y., Polyak, A. & Wolf, L. Unsupervised cross-domain image generation. in ICLR arXiv:1611.02200. (2017). https://openreview.net/forum?id=Sk2Im59ex
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 1125–1134 (2017).
Kingma, D. P., Ba, J. & Adam A Method for Stochastic Optimization. arXiv:1412.6980 (2014). https://ui.adsabs.harvard.edu/abs/2014arXiv1412.6980K
Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612. https://doi.org/10.1109/tip.2003.819861 (2004).
Zhang, R., Isola, P., Efros, A. A., Shechtman, E. & Wang, O. The unreasonable effectiveness of deep features as a perceptual metric. Proc. Cvpr Ieee 586–595. https://doi.org/10.1109/Cvpr.2018.00068 (2018).
Gao, Y., Liu, Y., Wang, Y., Shi, Z. & Yu, J. A. Universal intensity standardization method based on a many-to-one weak-paired cycle generative Adversarial network for magnetic resonance images. IEEE Trans. Med. Imaging. 38, 2059–2069. https://doi.org/10.1109/TMI.2019.2894692 (2019).
Fischl, B. FreeSurfer. Neuroimage 62, 774–781, doi:https://doi.org/10.1016/j.neuroimage.2012.01.021 (2012).
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2023R1A2C3003250). This study was supported by grant no. 0320230270 from the SNUH Research Fund and the Seoul National University Hospital GE Center (grant no. 1820230040). This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : RS-2023-00262321). This study was supported by the “Korea National Institute of Health“(KNIH) research project (project No. 2024-ER1004-00). This work was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS-2025-02307233).
Author information
Authors and Affiliations
Contributions
Conception and design: R.E.Y., S.H.C., J.K.S. Analysis and interpretation: H.S.H., H.U.C., H.J.J., H.W.L., S.W.J., Y.H.J., R.E.Y., S.H.C., J.K.S. Data collection: H.S.H., H.U.C., H.J.J., H.W.L. Writing the article: H.S.H., H.U.C., H.J.J., H.W.L., R.E.Y., J.K.S. Critical revision of the article: H.S.H., H.U.C., H.J.J., H.W.L., S.W.J., Y.H.J., R.E.Y., S.H.C., J.K.S. Final approval of the article H.S.H., H.U.C., H.J.J., H.W.L., S.W.J., Y.H.J., R.E.Y., S.H.C., J.K.S. Overall responsibility: R.E.Y., J.K.S.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Hwang, H., Choi, HU., Jeong, H. et al. Development of head-to-head and longitudinal CycleGAN algorithm for MRI harmonization: validation in follow-up MRI evaluation in patients with brain metastasis. Sci Rep (2026). https://doi.org/10.1038/s41598-026-43755-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-43755-7
