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Proceedings Paper

Pseudo CT estimation from MRI using patch-based random forest
Author(s): Xiaofeng Yang; Yang Lei; Hui-Kuo Shu; Peter Rossi; Hui Mao; Hyunsuk Shim; Walter J. Curran; Tian Liu
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Paper 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.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332Q (24 February 2017); doi: 10.1117/12.2253936
Show Author Affiliations
Xiaofeng Yang, Winship Cancer Institute, Emory Univ. (United States)
Yang Lei, Winship Cancer Institute, Emory Univ. (United States)
Hui-Kuo Shu, Winship Cancer Institute, Emory Univ. (United States)
Peter Rossi, Winship Cancer Institute, Emory Univ. (United States)
Hui Mao, Winship Cancer Institute, Emory Univ. (United States)
Hyunsuk Shim, Winship Cancer Institute, Emory Univ. (United States)
Walter J. Curran, Winship Cancer Institute, Emory Univ. (United States)
Tian Liu, Winship Cancer Institute, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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