Abstract #0221

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

Chi Zhang^1,2, Jinghan Jia³, Burhaneddin Yaman^1,2, Steen Moeller², Sijia Liu⁴, Mingyi Hong¹, and Mehmet Akçakaya^1,2

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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