Assessment of the generalization of learned image reconstruction and the potential for transfer learning

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An overview of deep learning in medical imaging focusing on MRI (2018, background): “In [137], the authors explored the potential for transfer learning (pretrained models) and assessed the generalization of learned image reconstruction regarding image contrast, SNR, sampling pattern and image content, using a variational network and true measurement k-space data from patient knee MR”
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction (2021, methodology): “The first: cascaded models with a data fidelity term and CNN regularization continue to dominate the submission field, as occurred in the previous challenge [11].”
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks (2020, unknown): “[33] studied the generalizability of the trained variational-net for the case of deviation between the training and testing data in terms of image contrast, signal-to-noise ratio (SNR), and image content.”
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data (2019, methodology): “e S1. The fully-sampled raw data were under-sampled retrospectively for both training and testing using equispaced sampling patterns provided in the fastMRI database with an acceleration rate (R) = 4 (27,58,59). The center of k-space was fully-sampled with 24 lines of auto-calibrated signal (ACS). T”