MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 12, 2019
- Source ID
- 10.1002/mp.13617
Entities
People
- Ashesh B. Jani
- Hui Mao
- Hui‐kuo Shu
- Joseph Harms
- Tian Liu
- Tonghe Wang
- Walter J. Curran
- Xiaofeng Yang
- Yang Lei
- Yingzi Liu
Organizations
- Emory University
- National Cancer Institute
- National Institutes of Health
- United States Department of Defense