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The ability to synthesise Computed Tomography images - commonly known aspseudo CT, or pCT - from MRI input data is commonly assessed using anintensity-wise similarity, such as an L2-norm between the ground truth CT andthe pCT. However, given that the ultimate purpose is often to use the pCT as anattenuation map ($\mu$-map) in Positron Emission Tomography Magnetic ResonanceImaging (PET/MRI), minimising the error between pCT and CT is not necessarilyoptimal. The main objective should be to predict a pCT that, when used as$\mu$-map, reconstructs a pseudo PET (pPET) which is as close as possible tothe gold standard PET. To this end, we propose a novel multi-hypothesis deeplearning framework that generates pCTs by minimising a combination of thepixel-wise error between pCT and CT and a proposed metric-loss that itself isrepresented by a convolutional neural network (CNN) and aims to minimisesubsequent PET residuals. The model is trained on a database of 400 pairedMR/CT/PET image slices. Quantitative results show that the network generatespCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT(69.68HU) compared to a baseline CNN (66.25HU), but lead to significantimprovement in the PET reconstruction - 115a.u. compared to baseline 140a.u.

Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning