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

这个项目将把PET和MR正则化图像重建算法重新设计为协同连接的变分神经网络(VNN)，并进一步利用深度生成模型作为每个VNN的先验。这两个网络将在每一层连接，以便允许同时进行PET和MR图像的协同重建。总体目标是为PET和MR重建提供增强的、协同的图像质量优势，此外，这些优势有可能使PET-MR成像更快、更便宜，甚至更安全(由于减少了辐射剂量)。

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).