2022
DOI: 10.48550/arxiv.2208.03161
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Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations

Abstract: Deep Learning (DL) methods have shown promising results for solving ill-posed inverse problems such as MR image reconstruction from undersampled k-space data. However, these approaches currently have no guarantees for reconstruction quality and the reliability of such algorithms is only poorly understood. Adversarial attacks offer a valuable tool to understand possible failure modes and worst case performance of DL-based reconstruction algorithms. In this paper we describe adversarial attacks on multi-coil k-s… Show more

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