mlsanityData to 13 Oct 2026

On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations

arXiv 2102.13066 ↗First posted 25 Feb 2021eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

Although deep learning (DL) has received much attention in accelerated MRI, recent studies suggest small perturbations may lead to instabilities in DL-based reconstructions, leading to concern for their clinical application. However, these works focus on single-coil acquisitions, which is not practical. We investigate instabilities caused by small adversarial attacks for multi-coil acquisitions. Our results suggest that, parallel imaging and multi-coil CS exhibit considerable instabilities against small adversarial perturbations.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 25 Feb 2021.

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12 of 19 references.

References · 12

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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 3.

4 papers.

  1. 2021A review and experimental evaluation of deep learning methods for MRI reconstructioncited by 13
  2. 2023Robust Physics-based Deep MRI Reconstruction Via Diffusion Purificationcited by 4
  3. 2023SMUG: Towards robust MRI reconstruction by smoothed unrollingcited by 3
  4. 2023Robust MRI Reconstruction by Smoothed Unrolling (SMUG)cited by 1