Pruning Unrolled Networks (PUN) at Initialization for MRI Reconstruction Improves Generalization

More recently, deep learning has garnered considerable attention in medical imaging and has demonstrated superior Deep learning methods are highly effective for many image performance in a variety of image reconstruction tasks including reconstruction tasks. However, the performance of supervised X-ray computed tomography [7], positron emission learned models can degrade when applied to distinct tomography [8], and MRI [9]. An important recent trend experimental settings at test time or in the presence of distribution in supervised deep learning for MRI is the development of shifts. In this study, we demonstrate that pruning unrolled networks. While common deep learning architectures deep image reconstruction networks at training time can improve such as U-Nets [10] and transformers [11] have been their robustness to distribution shifts. In particular, we highly successful in MR image reconstruction, they do not consider unrolled reconstruction architectures for accelerated directly incorporate knowledge of the forward model of the magnetic resonance imaging and introduce a method for pruning imaging system (i.e. the underlying physics) into the reconstruction unrolled networks (PUN) at initialization.