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<p>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&#xa0;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&#xa0;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.</p>

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

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Hanshuo Zhu,
  • Xiaozhen Ren,
  • Xiaqiong Fan,
  • Zhipeng Guo,
  • Tiejun Yang

摘要

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.