Motivation: MRI navigation for intervention requires higher imaging efficiency, and deep learning-based algorithms significantly reduce reconstruction time. Goal(s): To develop a deep learning-based MRI reconstruction method suitable for interventional navigation tasks. Approach: This study proposes an unfolded neural network to reconstruct radial sampling sequences, integrating the data consistency term with both parallel and serial network structures, and incorporating a data-sharing term at the beginning. Results: Under limited computational resources, the proposed method demonstrates superior performance compared to the comparative methods on two tested datasets. Impact: We propose a promising MRI reconstruction algorithm suitable for navigation scenarios, which will help advance our goals in MRI-guided surgeries.
Zhong et al. (Tue,) studied this question.
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