Skip to main content
 
Notice: Users may be experiencing issues with displaying some pages on stanfordhealthcare.org. We are working closely with our technical teams to resolve the issue as quickly as possible. Thank you for your patience.
Stanford Health Care

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

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging. Proceedings. IEEE International Symposium on Biomedical Imaging Nakarmi, U., Cheng, J. Y., Rios, E. P., Mardani, M., Pauly, J. M., Ying, L., Vasanawala, S. S. 2020; 2020: 1056–59

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 multi-scale 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.

View details for DOI 10.1109/isbi45749.2020.9098684

View details for PubMedID 33282118