MRI-based pseudo CT synthesis using anatomical signature and alternating random forest with iterative refinement model

• Type of Material: Article
• Authors: Yang Lei, Emory University; Jiwoong Jason Jeong, Emory University; Tonghe Wang, Emory University; Hui-Kuo Shu, Emory University; Pretesh Patel, Emory University; Sibo Tian, Emory University; Tian Liu, Emory University; Hyunsuk Shim, Emory University; Hui Mao, Emory University; Ashesh B Jani, Emory University; Walter J Curran, Emory University; Xiaofeng Yang, Emory University
• Language: English
• Date: 2018-10-01
• Publisher: Society of Photo-optical Instrumentation Engineers (SPIE)
• Publication Version: Final Published Version
• Copyright Statement: © 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).
• Final Published Version (URL): http://dx.doi.org/10.1117/1.JMI.5.4.043504
• Title of Journal or Parent Work: Journal of Medical Imaging
• ISSN: 2329-4302
• Volume: 5
• Issue: 4
• Start Page: 043504
• End Page: 043504
• Grant/Funding Information: This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award No. R01CA215718; and the Department of Defense (DoD) Prostate Cancer Research Program (PCRP) Award No. W81XWH-13-1-0269.
• Keywords: MRI-based treatment planning; feature selection; pseudo computed tomography; random forest
• Research Categories: Physics, Radiation; Health Sciences, Radiology

Abstract

We develop a learning-based method to generate patient-specific pseudo computed tomography (CT) from routinely acquired magnetic resonance imaging (MRI) for potential MRI-based radiotherapy treatment planning. The proposed pseudo CT (PCT) synthesis method consists of a training stage and a synthesizing stage. During the training stage, patch-based features are extracted from MRIs. Using a feature selection, the most informative features are identified as an anatomical signature to train a sequence of alternating random forests based on an iterative refinement model. During the synthesizing stage, we feed the anatomical signatures extracted from an MRI into the sequence of well-trained forests for a PCT synthesis. Our PCT was compared with original CT (ground truth) to quantitatively assess the synthesis accuracy. The mean absolute error, peak signal-to-noise ratio, and normalized cross-correlation indices were 60.87 ± 15.10 HU, 24.63 ± 1.73 dB, and 0.954 ± 0.013 for 14 patients' brain data and 29.86 ± 10.4 HU, 34.18 ± 3.31 dB, and 0.980 ± 0.025 for 12 patients' pelvic data, respectively. We have investigated a learning-based approach to synthesize CTs from routine MRIs and demonstrated its feasibility and reliability. The proposed PCT synthesis technique can be a useful tool for MRI-based radiation treatment planning.