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Abstract #1838

Joint Tensor Low-Rank and Attention-based Sparse Unrolling Network for Accelerating Dynamic MRI

Yue Hu1, Yinghao Zhang1, and Junsiyuan Li2
1Harbin Institute of Technology, Harbin, China, 2Department of Radiology, Sengkang General Hospital, Singapore Health Services, Singapore, Singapore

Synopsis

Keywords: Image Reconstruction, Cardiovascular

Motivation: Current unrolling networks that jointly utilize two priors in accelerating MRI adopted complex and nested iterative algorithm as their network structure foundation.

Goal(s): To propose a simple yet efficient algorithm for unrolling network that jointly uses two priors.

Approach: We propose a novel deep unrolling network, JotlasNet, for dynamic MRI reconstruction by jointly utilizing tensor low-rank and attention-based sparse priors. Based on the composite splitting algorithm, we design a simple yet efficient structure for the proposed JotlasNet.

Results: Extensive experiments on a cardiac cine MRI dataset demonstrate the superior performance of JotlasNet in dynamic MRI reconstruction.

Impact: The framework we proposed carries profound implications for various models incorporating joint priors, extending beyond the interaction of low-rank and sparse priors and transcending the realm of dynamic MRI reconstruction applications.

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