CT synthesis from MRI using multi-cycle GAN for head-and-neck radiation therapy. (July 2021)

• Record Type: Journal Article
• Authors: Liu, Yanxia; Chen, Anni; Shi, Hongyu; Huang, Sijuan; Zheng, Wanjia; Liu, Zhiqiang; Zhang, Qin; Yang, Xin
• Is Part Of: Computerized medical imaging and graphics. Volume 91(2021)
• Publication Date: 2021-07
• Subjects: Deep learning -- MRI -- CT synthesis -- Cycle GAN; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101953
• Languages: English
• ISSNs: 0895-6111

Abstract

Highlights: MRI to CT synthesis using unpaired data. Generating synthesis CT for Head-and-Neck radiation therapy. Obtaining high-precision results and retaining anatomy details. Optimization of Cycle GAN. Abstract: 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.