  Although deep learning (DL) has received much attention in accelerated
magnetic resonance imaging (MRI), recent studies show that tiny input
perturbations may lead to instabilities of DL-based MRI reconstruction models.
However, the approaches of robustifying these models are underdeveloped.
Compared to image classification, it could be much more challenging to achieve
a robust MRI image reconstruction network considering its regression-based
learning objective, limited amount of training data, and lack of efficient
robustness metrics. To circumvent the above limitations, our work revisits the
problem of DL-based image reconstruction through the lens of robust machine
learning. We find a new instability source of MRI image reconstruction, i.e.,
the lack of reconstruction robustness against spatial transformations of an
input, e.g., rotation and cutout. Inspired by this new robustness metric, we
develop a robustness-aware image reconstruction method that can defend against
both pixel-wise adversarial perturbations as well as spatial transformations.
Extensive experiments are also conducted to demonstrate the effectiveness of
our proposed approaches.
