Test-time augmentation (TTA) is explored as an uncertainty prediction method for a neural network based 3D motion artifact correction starting from magnitude images. To this end, a synthetic training dataset is generated using a dedicated 3D motion artifact simulation pipeline. After training, a TTA-based uncertainty metric is employed to predict the network performance for data not contained in training. Using synthetic test data, we find that the proposed method can accurately predict the overall motion correction accuracy (total RMSE) but fails in certain cases to reliably detect local “hallucinations” (brain-like structures different from the actual anatomy) of the network.
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