@inproceedings{c376ec29f3174703bde5695bc98a3bc1,
title = "Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging",
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.",
keywords = "deep learning, Magnetic resonance imaging, multi-scale CNN, unrolled network",
author = "Ukash Nakarmi and Cheng, \{Joseph Y.\} and Rios, \{Edgar P.\} and Morteza Mardani and Pauly, \{John M.\} and Leslie Ying and Vasanawala, \{Shreyas S.\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 ; Conference date: 03-04-2020 Through 07-04-2020",
year = "2020",
month = apr,
doi = "10.1109/ISBI45749.2020.9098684",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1056--1059",
booktitle = "ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}