A MRI-only treatment planning pipeline, deepMTP, was constructed using a deep learning approach to generate continuously-valued pseudo CT images from MR images. A deep convolutional neural network was designed to identify tissue features in volumetric head MR images training with co-registered kVCT images. A set of 40 retrospective 3D T1-weighted head images was utilized to train the model, and evaluated in 10 clinical cases with brain metastases. Statistical analysis was used to compare dosimetric parameters of plans made with pseudo CT images generated from deepMTP to those made with kVCT based clinical treatment plan, where no significant difference was found.
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