Keywords: Image Reconstruction, Parallel Imaging, Compressed Sensing, Deep Learning
Deep learning (DL) methods have enabled state-of-the-art reconstructions of magnetic resonance images of highly undersampled acquisitions. The end-to-end variational network (E2E VarNet) is a DL method that can output high quality reconstructions through an unrolled gradient descent algorithm. Nevertheless, the network discards a lot of high-level feature representations of the image to perform data consistency in the image space. Here, we adapted the E2E VarNet architecture to perform the data consistency in a feature space. We trained the proposed network using the fastMRI brain dataset and observed 0.0013 SSIM improvement for eight-fold accelerations.
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