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Abstract #4213

State of the ART (Adversarial Robust Training) to Reconstruct Clinically Relevant Features in Accelerated Knee MRI

Francesco Caliva1, Victor Kaiyang Cheng2, Rutwik Shah1, Misung Han1, Sharmila Majumdar1, and Valentina Pedoia1
1University of California San Francisco, San Francisco, CA, United States, 2University of California Berkeley, Berkeley, CA, United States

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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