In this work, we propose a novel neural network architecture named ‘ETER-net’ as a unified solution to reconstruct an MR image directly from k-space data. The proposed image reconstruction network can be applied to k-space data that are acquired with various scanning trajectories and multi or single-channel RF coils. It also can be used for semi-supervised domain adaptation. To evaluate the performance of the proposed method, it was applied to brain MR data obtained from a 3T MRI scanner with Cartesian and radial trajectories.
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