Multi-Frequency Feature Guided Progressive Divide-and-Conquer Network for Accelerated MRI Reconstruction

Magnetic resonance imaging (MRI) is regarded as the clinical diagnostic gold standard. However, its lengthy scan times introduce motion artifacts, which can severely compromise diagnostic accuracy. K-space undersampling is a fundamental strategy to address this issue, but undersampling inevitably introduces quality degradation in reconstructed images. To tackle the challenges in accelerated MRI reconstruction, this paper proposes a Multi-Feature Guided Progressive Divide-and-Conquer reconstruction network (MFG-PDAC). It achieves synergistic optimization through three novel modules. The Multi-Frequency Gated Attention (MFGA) module enhances feature propagation, the Edge Enhanced Feature Modulation (EEFM) module reinforces anatomical boundaries, and the Frequency-Aware Data Consistency (FREDC) module optimizes spectral reconstruction. These three modules form a closed-loop mechanism consisting of feature selection, spatial optimization, and frequency-domain correction. The MFGA enables dynamic fusion of multi-frequency features at U-Net skip connections, providing structural priors for gradient modulation. The gradient modulation amplifies edge response in the image domain, improving high-frequency reconstruction quality. The FREDC dynamically weights constraints based on frequency band errors, creating a feedback mechanism for MFGA refinement. Evaluated on the fast MRI knee dataset, MFG-PDAC achieved a peak signal-to-noise ratio of 37.28 dB and structural similarity index measurement of 0.909 under 8× acceleration, outperforming the current mainstream methods. The network particularly can achieve better reconstruction in key diagnostic regions such as bone-soft tissue interfaces and ligament textures. This study provides an accurate and efficient solution for clinical rapid MRI scanning, demonstrating significant potential for clinical translation.