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Abstract #0221

On Instabilities of Conventional Multi-Coil MRI Reconstruction To Small Adversarial Perturbations

Chi Zhang1,2, Jinghan Jia3, Burhaneddin Yaman1,2, Steen Moeller2, Sijia Liu4, Mingyi Hong1, and Mehmet Ak├žakaya1,2
1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 3University of Florida, Gainesville, FL, United States, 4MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, United States

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.

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