  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.
