Abstract
Arterial spin labeling (ASL) images begin to receive much popularity in dementia diseases diagnosis recently, yet it is still not commonly seen in well-established image datasets for investigating dementia diseases. Hence, synthesizing ASL images from available data is worthy of investigations. In this study, a novel locally-constrained WGAN-GP model ensemble is proposed to realize ASL images synthesis from structural MRI for the first time. Technically, this new WGAN-GP model ensemble is unique in its constrained optimization task, in which diverse local constraints are incorporated. In this way, more details of synthesized ASL images can be obtained after incorporating local constraints in this new ensemble. The effectiveness of the new WGAN-GP model ensemble for synthesizing ASL images has been substantiated both qualitatively and quantitatively through rigorous experiments in this study. Comprehensive analyses reveal that, this new WGAN-GP model ensemble is superior to several state-of-the-art GAN-based models in synthesizing ASL images from structural MRI in this study.
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References
Huang, Y., et al.: Cross-modality image synthesis via weakly coupled and geometry co-regularized joint dictionary learning. IEEE TMI 37(3), 815–827 (2018)
Duchateau, N., Sermesant, M., Delingette, H., Ayache, N.: Model-based generation of large databases of cardiac images: synthesis of pathological cine MR sequences from real healthy cases. IEEE TMI 37(3), 755–766 (2018)
Wang, Y., Zhou, L., Yu, B., et al.: 3D auto-context-based locality adaptive multi-modality GANs for PET synthesis. IEEE TMI 38(6), 1328–1339 (2018)
Zhou, Y., Giffard-Roisin, S., De Craene, M., et al.: A framework for the generation of realistic synthetic cardiac ultrasound and magnetic resonance imaging sequences from the same virtual patients. IEEE TMI 37(3), 741–754 (2018)
Costa, P., Galdran, A., Meyer, M., et al.: End-to-end adversarial retinal image synthesis. IEEE TMI 37(3), 781–791 (2018)
Goodfellow, I., et al.: Generative adversarial networks. arXiv:1406.2661 (2014)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv:1701.07875 (2017)
Gulrajani, I., Ahmed, F., Arjovsky, M., et al.: Improved training of wasserstein GANs, arXiv:1704.00028 (2017)
Huang, W.: A novel disease severity prediction scheme via big pair-wise ranking and learning techniques using image-based personal clinical data. Sign. Process. 124, 233–245 (2016)
Huang, W., et al.: Arterial Spin labeling images synthesis from sMRI using unbalanced deep discriminant learning. IEEE TMI (2019). https://doi.org/10.1109/TMI.2019.2906677
Huang, W., Zeng, S., Wan, M., Chen, G.: Medical media analytics via ranking and big learning: a multi-modality image-based disease severity prediction study. Neurocomputing 204, 125–134 (2016)
Mao, X., Li, Q., Xie, H., et al.: Least squares generative adversarial networks. arXiv:1611.04076 (2016)
Zhu, J., Park, T., Isola, P., Efros, A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv:1703.10593 (2017)
Rezaei, M., et al.: Conditional generative refinement adversarial networks for unbalanced medical image semantic segmentation. arXiv:1810.03871 (2018)
Acknowledgements
This work was supported by the grant 61862043 approved by National Natural Science Foundation of China, and the key grant 20181ACB20006 approved by Natural Science Foundation of Jiangxi Province.
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Huang, W., Luo, M., Liu, X., Zhang, P., Ding, H., Ni, D. (2019). Arterial Spin Labeling Images Synthesis via Locally-Constrained WGAN-GP Ensemble. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_84
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DOI: https://doi.org/10.1007/978-3-030-32251-9_84
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