Pseudo CT Estimation from MRI Using Patch-based Random Forest

• Type of Material: Article
• Authors: Xiaofeng Yang, Winship Cancer Institute; Yang Lei, Winship Cancer Institute; Hui-Kuo Shu, Winship Cancer Institute; Peter Rossi, Winship Cancer Institute; Hui Mao, Emory University; Hyunsuk Shim, Winship Cancer Institute; Walter J Curran, Emory University; Tian Liu, Winship Cancer Institute
• Language: English
• Date: 2017-02-01
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Final Published Version
• Copyright Statement: © (2017) 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/10.1117/12.2253936
• Title of Journal or Parent Work: Proceedings of SPIE
• ISSN: 0277-786X
• Volume: 10133
• Grant/Funding Information: This research is supported in part by 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: Pseudo CT; patch; MRI; random forest
• Research Categories: Health Sciences, Radiology; Health Sciences, Oncology

Abstract

Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.