Learning‐based CBCT correction using alternating random forest based on auto‐context model
Abstract
Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image‐guided radiotherapy because it provides a foundation for advanced image‐guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currently limited only to patient setup in the clinic because of the severe issues in its image quality. In this study, we develop a learning‐based approach to improve CBCT's image quality for extended clinical applications.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 11, 2018
- Source ID
- 10.1002/mp.13295
Entities
People
- Anees Dhabaan
- Jiwoong Jeong
- Jolinta Lin
- Kristin Higgins
- Robert Press
- Tian Liu
- Tonghe Wang
- Walter J. Curran
- Xiangyang Tang
- Xiaofeng Yang
- Xueming Dong
- Yang Lei
Organizations
- Emory University
- Georgia Tech
- National Cancer Institute
- United States Department of Defense