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Neural network (NN) based approaches for super-resolution MRI typicallyrequire high-SNR high-resolution reference data acquired in many subjects,which is time consuming and a barrier to feasible and accessibleimplementation. We propose to train NNs for Super-Resolution using NoisyReference data (SRNR), leveraging the mechanism of the classic NN-baseddenoising method Noise2Noise. We systematically demonstrate that results fromNNs trained using noisy and high-SNR references are similar for both simulatedand empirical data. SRNR suggests a smaller number of repetitions ofhigh-resolution reference data can be used to simplify the training datapreparation for super-resolution MRI.

SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data