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1 March 2019 MRI-based pseudo CT generation using classification and regression random forest
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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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yang Lei, Tonghe Wang, Joseph Harms, Ghazal Shafai-Erfani, Sibo Tian, Kristin Higgins, Hui-Kuo Shu, Hyunsuk Shim, Hui Mao, Walter J. Curran, Tian Liu, and Xiaofeng Yang "MRI-based pseudo CT generation using classification and regression random forest", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094843 (1 March 2019); https://doi.org/10.1117/12.2512560
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