Self-supervised reconstruction methods for undersampled acquisitions are becoming increasingly used. We compare different self-supervised reconstruction methods using fully sampled and prospectively/retrospectively accelerated data; we find that prospective and retrospective reconstructions can differ significantly in quantitative metrics and perceptual quality. To test the methods’ generalizability, prospectively accelerated data from multiple field strengths is reconstructed without retraining/retuning. We find that no-reference image quality metrics can distinguish state of the art methods from the baseline, albeit with ambiguity between the state of the art methods.
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