Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging

Ukash Nakarmi, Joseph Y. Cheng, Edgar P. Rios, Morteza Mardani, John M. Pauly, Leslie Ying, Shreyas S. Vasanawala

Abstract: Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-centric deep learning frameworks due to its fast inference time and `one-parameter-fit-all' principle unlike in traditional model-based acceleration techniques. Unrolled deep learning framework that combines the deep priors and model knowledge are robust compared to naive deep learning based framework. In this paper, we propose a novel multiscale unrolled deep learning framework which learns deep image priors through multi-scale CNN and is combined with unrolled framework to enforce data-consistency and model knowledge. Essentially, this framework combines the best of both learning paradigms:model-based and data-centric learning paradigms. Proposed method is verified using several experiments on numerous data sets.