One of the key limitations in conventional deep learning-based MR image reconstruction is the need for registered pairs of training images, where fullysampled ground truth images are required as the target. We address this limitation by proposing a novel registration-augmented image reconstruction method that trains a CNN by directly mapping pairs of unregistered and undersampled MR measurements. The proposed method is validated on a single-coil MRI data set by training a model directly on pairs of undersampled measurements from images that have undergone nonrigid deformations.
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