OBJECTIVE: Magnetic resonance imaging (MRI) is widely used for its excellent soft-tissue contrast and non-ionizing nature, but its long acquisition time remains a major bottleneck. APPROACH: In this work, we propose Mamba2SVN, a variational network that couples a Mamba2-based reconstruction branch with reconstruction-cooperative sensitivity refinement. First, we design a Mamba2-based Cross-iteration Feature Reconstruction (MCFR) module, where MCFRNet integrates Mamba2 blocks into a U-shaped backbone and incorporates a cross-iteration feature fusion (CIFF) mechanism. A preprocessing gated fusion (PPGF) unit adaptively fuses current bottleneck features with prior CIFF features, and wavelet-based preprocessing and postprocessing units (PRU/POU) balance reconstruction accuracy and computational efficiency. Second, we introduce a reconstruction-cooperative sensitivity refinement (RCSR) module that iteratively refines latent sensitivity variables by leveraging both previously reconstructed multi-coil k-space data and prior estimates of these variables, thereby reducing dependence on autocalibration signal (ACS) size. Third, we impose a dual data-consistency (DDC) mechanism that employs two complementary data-consistency operators at each iteration to alleviate error accumulation. RESULTS: Extensive experiments on multi-coil knee, brain, and cardiac datasets with various undersampling patterns demonstrate that Mamba2SVN consistently outperforms state-of-the-art deep learning-based methods. SIGNIFICANCE: Mamba2SVN also remains robust when the ACS region is very small, suggesting its potential for highly accelerated clinical MRI protocols.
Li et al. (Thu,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: