Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography
Abstract
The incorporation of cone‐beam computed tomography (CBCT) has allowed for enhanced image‐guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning‐based method for generating high quality corrected CBCT (CCBCT) images is proposed.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jul 17, 2019
- Source ID
- 10.1002/mp.13656
Entities
People
- Joseph Harms
- Jun Zhou
- Rongxiao Zhang
- Tian Liu
- Tonghe Wang
- Walter J. Curran
- Xiangyang Tang
- Xiaofeng Yang
- Yang Lei
Organizations
- Emory University
- National Institutes of Health
- United States Department of Defense