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

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.