In this work, we demonstrate a generic deep learning (DL) model that computes pCT images (i.e. continuous density bone) using a single channel ZTE MRI data and is robust to protocol and coil variations (as dictated by application needs). The method was evaluated for PET/MR attenuation correction protocol (low resolution for speed) and MRgRTP dose planning protocol (higher resolution for spatial accuracy). The advantages include a single model for multiple protocols, pCT which are very much like real CT in appearance, as well as excellent quantitative accuracy of estimated bone values in the computed pCT.
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