  Reconstructing under-sampled k-space measurements in Compressed Sensing MRI
(CS-MRI) is classically solved with regularized least-squares. Recently, deep
learning has been used to amortize this optimization by training reconstruction
networks on a dataset of under-sampled measurements. Here, a crucial design
choice is the regularization function(s) and corresponding weight(s). In this
paper, we explore a novel strategy of using a hypernetwork to generate the
parameters of a separate reconstruction network as a function of the
regularization weight(s), resulting in a regularization-agnostic reconstruction
model. At test time, for a given under-sampled image, our model can rapidly
compute reconstructions with different amounts of regularization. We analyze
the variability of these reconstructions, especially in situations when the
overall quality is similar. Finally, we propose and empirically demonstrate an
efficient and data-driven way of maximizing reconstruction performance given
limited hypernetwork capacity. Our code is publicly available at
https://github.com/alanqrwang/RegAgnosticCSMRI.
