Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Deep Learning ReconstrcutionDeep neural networks (DNNs) for MRI reconstruction often require large datasets for training, but in clinical settings, the domains of datasets are diverse, and the degree to which deep neural networks are the robustness of DNNs to domain differences between training and testing datasets has been an open question. Here, we evaluated the robustness of four open-source multicoil networks to differences in the domain. We found that model-based networks exhibit higher robustness than data-driven networks and that robustness varies across network architectures, even within model-based networks. Our results provide insight into what network architectures are effective for generalization performance.
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