Pseudo CT estimation from MRI using patch-based random forest

Xiaofeng Yang; Yang Lei; Hui-Kuo Shu; Peter Rossi; Hui Mao; Hyunsuk Shim; Walter J. Curran; Tian Liu

doi:10.1117/12.2253936

24 February 2017 Pseudo CT estimation from MRI using patch-based random forest

Xiaofeng Yang, Yang Lei, Hui-Kuo Shu, Peter Rossi, Hui Mao, Hyunsuk Shim, Walter J. Curran, Tian Liu

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101332Q (2017) https://doi.org/10.1117/12.2253936
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Citation Download Citation

Xiaofeng Yang, Yang Lei, Hui-Kuo Shu, Peter Rossi, Hui Mao, Hyunsuk Shim, Walter J. Curran, and Tian Liu "Pseudo CT estimation from MRI using patch-based random forest", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332Q (24 February 2017); https://doi.org/10.1117/12.2253936

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