论文标题
使用基于转换的张量低级网络（LT $^2 $ LR-NET）的动态MRI
Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT$^2$LR-Net)
论文作者
论文摘要
虽然低排名矩阵先验已在动态MR图像重建中被利用并获得了令人满意的性能，但张量低级别模型最近已成为三维动态MR数据集的强大替代表示。在本文中，我们引入了一个用于动态MRI的新型深层展开网络，即基于转换的张量低率网络（LT $^2 $ LR-NET）。首先，我们将张量奇异值分解（T-SVD）推广到基于单一的统一变换版本中，然后提出了新颖的转换张量核定常（TTNN）。然后，我们基于乘数的交替方向方法（ADMM）设计了一种基于TTNN的新型迭代优化算法，以利用转换的域中的张量低量表。将相应的迭代步骤展开到所提出的LT $^2 $ LR-NET中，其中卷积神经网络（CNN）被合并，以适应从动态MR数据集中学习转换，以进行更稳健，准确的张量低量表。心脏Cine MR数据集的实验结果表明，与最先进的方法相比，提出的框架可以提供改进的恢复结果。
While low-rank matrix prior has been exploited in dynamic MR image reconstruction and has obtained satisfying performance, tensor low-rank models have recently emerged as powerful alternative representations for three-dimensional dynamic MR datasets. In this paper, we introduce a novel deep unrolling network for dynamic MRI, namely the learned transform-based tensor low-rank network (LT$^2$LR-Net). First, we generalize the tensor singular value decomposition (t-SVD) into an arbitrary unitary transform-based version and subsequently propose the novel transformed tensor nuclear norm (TTNN). Then, we design a novel TTNN-based iterative optimization algorithm based on the alternating direction method of multipliers (ADMM) to exploit the tensor low-rank prior in the transformed domain. The corresponding iterative steps are unrolled into the proposed LT$^2$LR-Net, where the convolutional neural network (CNN) is incorporated to adaptively learn the transformation from the dynamic MR dataset for more robust and accurate tensor low-rank representations. Experimental results on the cardiac cine MR dataset demonstrate that the proposed framework can provide improved recovery results compared with the state-of-the-art methods.
