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Abstract #3909

Understanding domain shift in learned MRI reconstruction: A quantitative analysis on fastMRI knee and neuro sequences

Shizhe He1,2, Veronika Anne Zimmer1, Daniel Rueckert1,3, and Kerstin Hammernik1,3
1Lab for Artificial Intelligence in Healthcare and Medicine, Technical University of Munich, Munich, Germany, 2Otto-von-Taube-Gymnasium, Gauting, Germany, 3Department of Computing, Imperial College London, London, United Kingdom


In this work, we investigate the problem of domain shift in the context of state-of-the-art MRI reconstruction networks with respect to variations in training data. We provide visualization tools and support our findings with statistical analysis for the networks evaluated on fastMRI knee and neuro data. We observe that the signal-to-noise ratio of the examined sequences plays an essential role, and we statistically prove the hypothesis that the type/amount of training data is less important for low acceleration factors. Finally, we provide a visualization tool facilitating the examination of the networks’ performance on each individual subject of the fastMRI data.

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