Multimodal MRI synthesis using unified generative adversarial networks
Abstract
Complementary information obtained from multiple contrasts of tissue facilitates physicians assessing, diagnosing and planning treatment of a variety of diseases. However, acquiring multiple contrasts magnetic resonance images (MRI) for every patient using multiple pulse sequences is time‐consuming and expensive, where, medical image synthesis has been demonstrated as an effective alternative. The purpose of this study is to develop a unified framework for multimodal MR image synthesis.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 27, 2020
- Source ID
- 10.1002/mp.14539
Entities
People
- Hui Mao
- Tian Liu
- Walter J. Curran
- Xianjin Dai
- Xiaofeng Yang
- Yabo Fu
- Yang Lei
Organizations
- Emory University
- National Cancer Institute Egypt
- National Institutes of Health
- United States Department of Defense