MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks

• Authors: Yang Lei, Emory University; Joseph Harms, Emory University; Tonghe Wang, Emory University; Yingzi Liu, Emory University; Hui-Kuo Shu, Emory University; Ashesh B Jani, Emory University; Walter J Curran, Emory University; Hui Mao, Emory University; Tian Liu, Emory University; Xiaofeng Yang, Emory University
• Journal Title: MEDICAL PHYSICS
• Volume: Volume 46, Number 8
• Publisher: WILEY | 2019-08-01, Pages 3565-3581
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1002/mp.13617
• Subjects: Health Sciences, Oncology; Health Sciences, Radiology
• Keywords: Science & Technology; Life Sciences & Biomedicine; Radiology, Nuclear Medicine & Medical Imaging; cycle consistent generative adversarial networks; deeply supervised network; dense convolutional networks; MRI-only based radiotherapy; synthetic CT; DERIVE ELECTRON-DENSITY; COMPUTED-TOMOGRAPHY; PSEUDO-CT; QUALITY-ASSURANCE; RADIOTHERAPY; IMAGE; REGISTRATION; ACCURACY; HEAD; DELINEATION

Abstract

Purpose: Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI-only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the patient treatment workflow. In this work, the authors propose a novel method for generation of sCT based on dense cycle-consistent generative adversarial networks (cycle GAN), a deep-learning based model that trains two transformation mappings (MRI to CT and CT to MRI) simultaneously. Methods and materials: The cycle GAN-based model was developed to generate sCT images in a patch-based framework. Cycle GAN was applied to this problem because it includes an inverse transformation from CT to MRI, which helps constrain the model to learn a one-to-one mapping. Dense block-based networks were used to construct generator of cycle GAN. The network weights and variables were optimized via a gradient difference (GD) loss and a novel distance loss metric between sCT and original CT. Results: Leave-one-out cross-validation was performed to validate the proposed model. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross correlation (NCC) indexes were used to quantify the differences between the sCT and original planning CT images. For the proposed method, the mean MAE between sCT and CT were 55.7 Hounsfield units (HU) for 24 brain cancer patients and 50.8 HU for 20 prostate cancer patients. The mean PSNR and NCC were 26.6 dB and 0.963 in the brain cases, and 24.5 dB and 0.929 in the pelvis. Conclusion: We developed and validated a novel learning-based approach to generate CT images from routine MRIs based on dense cycle GAN model to effectively capture the relationship between the CT and MRIs. The proposed method can generate robust, high-quality sCT in minutes. The proposed method offers strong potential for supporting near real-time MRI-only treatment planning in the brain and pelvis.