Architecture-agnostic Deep Image Prior for Accelerated MRI reconstruction

This work aims to simplify deep image prior (DIP) architectural design decisions in the context of unsupervised accelerated MRI reconstruction, facilitating the deployment of MRI-DIP in real-world settings. We first show that architectures inappropriate for specific MRI datasets (knee, brain) can lead to severe reconstruction artifacts, and then demonstrate that proper network regularization can dramatically improve image quality irrespective of network architectures. The proposed plug-and-play regularization is applicable to any network form without architectural modifications.