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While low-rank matrix prior has been exploited in dynamic MR imagereconstruction and has obtained satisfying performance, tensor low-rank modelshave recently emerged as powerful alternative representations forthree-dimensional dynamic MR datasets. In this paper, we introduce a novel deepunrolling network for dynamic MRI, namely the learned transform-based tensorlow-rank network (LT$^2$LR-Net). First, we generalize the tensor singular valuedecomposition (t-SVD) into an arbitrary unitary transform-based version andsubsequently propose the novel transformed tensor nuclear norm (TTNN). Then, wedesign a novel TTNN-based iterative optimization algorithm based on thealternating direction method of multipliers (ADMM) to exploit the tensorlow-rank prior in the transformed domain. The corresponding iterative steps areunrolled into the proposed LT$^2$LR-Net, where the convolutional neural network(CNN) is incorporated to adaptively learn the transformation from the dynamicMR dataset for more robust and accurate tensor low-rank representations.Experimental results on the cardiac cine MR dataset demonstrate that theproposed framework can provide improved recovery results compared with thestate-of-the-art methods.

Dynamic MRI using Learned Transform-based Tensor Low-Rank Network (LT$^2$LR-Net)