  Unrolled neural networks have enabled state-of-the-art reconstruction
performance and fast inference times for the accelerated magnetic resonance
imaging (MRI) reconstruction task. However, these approaches depend on
fully-sampled scans as ground truth data which is either costly or not possible
to acquire in many clinical medical imaging applications; hence, reducing
dependence on data is desirable. In this work, we propose modeling the proximal
operators of unrolled neural networks with scale-equivariant convolutional
neural networks in order to improve the data-efficiency and robustness to
drifts in scale of the images that might stem from the variability of patient
anatomies or change in field-of-view across different MRI scanners. Our
approach demonstrates strong improvements over the state-of-the-art unrolled
neural networks under the same memory constraints both with and without data
augmentations on both in-distribution and out-of-distribution scaled images
without significantly increasing the train or inference time.
