Deep Plug-and-Play Prior for Parallel MRI Reconstruction

In this research, we propose a learning-based plug-and-play prior framework for parallel MRI reconstruction which extends the framework to its data-adaptive variant and provides an end-to-end reconstruction scheme.

We demonstrate that a deep plug-and-play prior framework for parallel MRI reconstruction with a regularization that adapts to the data itself results in excellent reconstruction accuracy and outperforms the clinical gold standard GRAPPA method.

Deep Plug-and-Play Prior for Parallel MRI Reconstruction PDF