CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy

作者：Liu, Yanxia; Chen, Anni; Shi, Hongyu; Huang, Sijuan; Zheng, Wanjia; Liu, Zhiqiang; Zhang, Qin^*; Yang, Xin^*

来源：COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2021, 91: 101953.

DOI：10.1016/j.compmedimag.2021.101953

• Deep learning
• MRI
• CT synthesis
• Cycle GAN

Magnetic Resonance Imaging (MRI) guided Radiation Therapy is a hot topic in the current studies of radiotherapy planning, which requires using MRI to generate synthetic Computed Tomography (sCT). Despite recent progress in image-to-image translation, it remains challenging to apply such techniques to generate high-quality medical images. This paper proposes a novel framework named Multi-Cycle GAN, which uses the Pseudo-Cycle Consistent module to control the consistency of generation and the domain control module to provide additional identical constraints. Besides, we design a new generator named Z-Net to improve the accuracy of anatomy details. Extensive experiments show that Multi-Cycle GAN outperforms state-of-the-art CT synthesis methods such as Cycle GAN, which improves MAE to 0.0416, ME to 0.0340, PSNR to 39.1053.

• 单位
  Sun Yat-sen University