Improving Image Quality of Cone-Beam CT Using Alternating Regression Forest

• Type of Material: Article
• Authors: Yang Lei, Emory University; Xiangyang Tang, Emory University; Kristin Higgins, Emory University; Tonghe Wang, Emory University; Tian Liu, Emory University; Anees Dhabaan, Emory University; Hyunsuk Shim, Emory University; Walter J Curran, Emory University; Xiaofeng Yang, Emory University
• Language: English
• Date: 2018-03-09
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Final Published Version
• Copyright Statement: © (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
• Final Published Version (URL): http://dx.doi.org/10.1117/12.2292886
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Volume: 10573
• Grant/Funding Information: This research is supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718; the Department of Defense (DoD) Prostate Cancer Research Program (PCRP) Award W81XWH-13-1-0269; and Dunwoody Golf Club Prostate Cancer Research Award, a philanthropic award provided by the Winship Cancer Institute of Emory University.
• Keywords: artifact correction; random forest; cone-beam CT; auto-context model
• Research Categories: Physics, Radiation; Health Sciences, Radiology

Abstract

We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The well-trained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients’ data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.