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MRI-based pseudo CT generation using classification and regression random forest
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Paper Abstract

We propose a method to generate patient-specific pseudo CT (pCT) from routinely-acquired MRI based on semantic information-based random forest and auto-context refinement. Auto-context model with patch-based anatomical features are integrated into classification forest to generate and improve semantic information. The concatenate of semantic information with anatomical features are then used to train a series of regression forests based on auto-context model. The pCT of new arrival MRI is generated by extracting anatomical features and feeding them into the well-trained classification and regression forests for pCT prediction. This proposed algorithm was evaluated using 11 patients’ data with brain MR and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross correlation (NCC) are 57.45±8.45 HU, 28.33±1.68 dB, and 0.97±0.01. The Dice similarity coefficient (DSC) for air, soft-tissue and bone are 97.79±0.76%, 93.32±2.35% and 84.49±5.50%, respectively. We have developed a novel machine-learning-based method to generate patient-specific pCT from routine anatomical MRI for MRI-only radiotherapy treatment planning. This pseudo CT generation technique could be a useful tool for MRI-based radiation treatment planning and MRI-based PET attenuation correction of PET/MRI scanner.

Paper Details

Date Published: 1 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094843 (1 March 2019); doi: 10.1117/12.2512560
Show Author Affiliations
Yang Lei, Emory Univ. (United States)
Tonghe Wang, Emory Univ. (United States)
Joseph Harms, Emory Univ. (United States)
Ghazal Shafai-Erfani, Emory Univ. (United States)
Sibo Tian, Emory Univ. (United States)
Kristin Higgins, Emory Univ. (United States)
Hui-Kuo Shu, Emory Univ. (United States)
Hyunsuk Shim, Emory Univ. (United States)
Hui Mao, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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