Fig. 1:

Information flow through our network. During a forward pass, true or estimated motion parameters $m$ serve as input to hypernetwork $h\left(\cdot ;{\theta }_h\right)$ which generates the weights ${\theta }_g$ of a reconstruction subnetwork $g\left(\cdot ;{\theta }_g\right)$. Reconstruction subnetwork $g\left(\cdot ;{\theta }_g\right)$ takes corrupted k-space data as input and produces a reconstruction. The hypernetwork weights ${\theta }_h$ are the only network parameters directly updated during training. At test-time, we freeze ${\theta }_h$ and use the data consistency loss to optimize the motion parameters $m$.