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Abstract #3593

A direct MR image reconstruction from k-space via End-To-End reconstruction network using recurrent neural network (ETER-net)

Changheun Oh1, yeji han2, and HyunWook Park1
1Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, 2Biomedical engineering, Gachon University, Incheon, Korea, Republic of

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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