Abstract
Accurate MR-to-CT synthesis plays an important role in MRI-only radiotherapy treatment planning. In medical image synthesis, the cycle-generative adversarial network (CycleGAN) is becoming an influential method, however, its image quality of synthesis is not optimal yet. In this study, we proposed a new learning method named U-Net-CycleGAN (UC-GAN) to generate synthetic CT (sCT) image for MRI-only radiation treatment planning, which integrated an improved U-Net concept into the original CycleGAN framework. After experimental comparison, The MAE value and PSNR of our UC-GAN model are 76.7 ± 4.5 and 46.1 ± 1.5, respectively, which are statistics significantly better than the 94.0 ± 4.3 (MAE) and 45.1 ± 1.5 (PSNR) of the original CycleGAN model. The results of our quantitative evaluation show that the UC-GAN model can synthesize a CT image closer to the reference real CT image with better performance.
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Acknowledgments
This work was supported in part by Shenzhen Key Basic Science Program (JCYJ20170413162213765 and JCYJ20180507182437217), the Shenzhen Key Laboratory Program (ZDSYS201707271637577), the NSFC-Shenzhen Union Program (U1613221), and the National Key Research and Development Program (2017YFC0110903).
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Wu, H., Jiang, X., Jia, F. (2019). UC-GAN for MR to CT Image Synthesis. In: Nguyen, D., Xing, L., Jiang, S. (eds) Artificial Intelligence in Radiation Therapy. AIRT 2019. Lecture Notes in Computer Science(), vol 11850. Springer, Cham. https://doi.org/10.1007/978-3-030-32486-5_18
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DOI: https://doi.org/10.1007/978-3-030-32486-5_18
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