Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceTensor low-rank models have recently emerged as powerful alternative representations for three-dimensional dynamic MR datasets. In this paper, we introduce a novel learned transform-based tensor low-rank network for dynamic MRI based on the tensor singular value decomposition (t-SVD). Instead of manually designing the t-SVD-based transform, we propose to utilize CNN to adaptively learn the relatively optimal transformation from the dynamic MR dataset for more robust and accurate tensor low-rank representations. Experimental results on cardiac cine MRI reconstruction demonstrate the superior performance of the proposed framework compared with the state-of-the-art methods.
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