Nakarmi Ukash, Cheng Joseph Y, Rios Edgar P, Mardani Morteza, Pauly John M, Ying Leslie, Vasanawala Shreyas S
Department Electrical Engineering.
Department of Radiology.
Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1056-1059. doi: 10.1109/isbi45749.2020.9098684. Epub 2020 May 22.
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.
由于磁共振成像（MRI）数据采集过程极其缓慢，加快其数据采集一直是长期以来备受关注的问题。与传统的基于模型的加速技术不同，近期加速MRI的趋势采用以数据为中心的深度学习框架，因为其推理时间快且具有“一参数适用于所有情况”的原则。与基于朴素深度学习的框架相比，结合深度先验和模型知识的展开式深度学习框架更稳健。在本文中，我们提出了一种新颖的多尺度展开式深度学习框架，该框架通过多尺度卷积神经网络学习深度图像先验，并与展开式框架相结合以强化数据一致性和模型知识。从本质上讲，该框架结合了两种学习范式的优点：基于模型的学习范式和以数据为中心的学习范式。所提出的方法通过在众多数据集上进行的若干实验得到了验证。
