Biswas Sampurna, Aggarwal Hemant K, Poddar Sunrita, Jacob Mathews
Department of Electrical and Computer Engineering, The University of Iowa, IA, USA.
Proc IEEE Int Conf Acoust Speech Signal Process. 2018 Apr;2018:6533-6537. doi: 10.1109/icassp.2018.8462637. Epub 2018 Sep 13.
We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) cardiac MRI from highly undersampled measurements. The DL priors enable us to exploit the local correlations, while the STORM prior enables us to make use of the extensive non-local similarities that are subject dependent. We introduce a novel model-based formulation that allows the seamless integration of deep learning methods with available prior information, which current deep learning algorithms are not capable of. The experimental results demonstrate the preliminary potential of this work in accelerating FBU cardiac MRI.
我们引入了一种基于模型的重建框架，该框架具有深度学习（DL）和流形上的平滑正则化（STORM）先验，用于从高度欠采样的测量中恢复自由呼吸和非门控（FBU）心脏磁共振成像（MRI）。DL先验使我们能够利用局部相关性，而STORM先验使我们能够利用依赖于个体的广泛非局部相似性。我们引入了一种新颖的基于模型的公式，它允许将深度学习方法与可用的先验信息无缝集成，而这是当前深度学习算法无法做到的。实验结果证明了这项工作在加速FBU心脏MRI方面的初步潜力。
