Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks
This work provides a more flexible and efficient approach to CS-MRI reconstruction for medical imaging practitioners by removing the need for manual regularization weight tuning, which is an incremental improvement.
This paper addresses the challenge of selecting appropriate regularization weights in Compressed Sensing MRI (CS-MRI) reconstruction by proposing a hypernetwork that generates parameters for a reconstruction network based on regularization weights. This allows for rapid computation of reconstructions with varying regularization at test time, and the authors demonstrate an efficient method for maximizing performance with limited hypernetwork capacity.
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.