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

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.

Automated summary

The study introduced a novel framework called Multi-Cycle GAN for generating high-quality medical images, incorporating a new generator called Z-Net to improve accuracy of anatomical details. Extensive experiments demonstrated that Multi-Cycle GAN outperformed state-of-the-art CT synthesis methods and achieved significant improvements in multiple metrics.