We propose an Adversarial Robust Training (ART) strategy to overcome the problem with accelerated MRI models, which are prone to missing small yet clinically relevant features. We introduced small, difficult to reconstruct synthetic features to undersampled MRIs and encouraged their reconstruction through robust training. To assess generalizability of our technique to real world applications, we annotated morphological features relevant to musculoskeletal disease diagnosis on images in the FastMRI dataset and tested ART. Overall, the approach has potential to reduce network instability and improve reliability and fidelity in image reconstruction.
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