Synergistic joint variational neural networks for PET-MR image reconstruction with generative modelling priors

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This project will redesign PET and MR regularised image reconstruction algorithms as synergistically-connected variational neural networks (VNNs), and furthermore make use of deep generative models as priors for each of the VNNs. Both networks will be connected at each layer in order to permit synergistic reconstruction of both PET and MR images simultaneously. The overall aim is to deliver enhanced, synergistic image quality benefits for both PET and MR reconstruction, and furthermore these benefits have potential to make PET-MR imaging faster, cheaper and even safer (due to reduced radiation doses).