Motion-mitigated reconstruction of accelerated MRI by using an unfolded variational network

Motion-mitigated reconstruction of highly undersampled MRI was achieved by adding a motion estimation module to the data consistency part of the model-based unfolded variation network. The motion estimation module consisted of a pair of convolutional blocks with residual inputs and added only limited number of trainable parameters to the network. The network was trained and tested on synthesized motion-corrupted images from a publicly available knee dataset. The reconstructed images with the proposed motion estimation module were sharper, and details were better recovered, with the structural similarity and peak signal-to-noise ratio significantly improved.