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<p>In order to enhance the quality of reconstructed images, a probabilistic undersampling mask optimization with weight adaptation for magnetic resonance imaging (MRI) reconstruction network (PUOR-Net) is proposed based on the undersampling mask optimization strategy and the iterative shrinkage-thresholding algorithm. PUOR-Net comprises two modules: the undersampling subnet and the reconstruction subnet. Within the undersampling subnet, PUOR-Net dynamically generates an optimized undersampling mask through adaptive weight adjustment mechanisms, overcoming the limitations of traditional methods reliant on pre-designed undersampling mask. The reconstruction subnet integrates complex convolution and channel attention mechanisms to further leverage data potential and accurately recover image detail. Experimental results show that PUOR-Net outperforms better on two MRI datasets than the traditional method using the fixed undersampling mask, which verifies the effectiveness of its adaptive optimization strategy. In addition, PUOR-Net demonstrates performance advantages compared to similar optimized mask joint learning networks.</p>

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Probabilistic Undersampling Mask Optimization with Weight Adaptation for MRI Reconstruction

  • 期刊论文

  • 发表日期：

    2025年6月21日

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  • Jizhong Duan,
  • Qinyao Zhu

摘要

In order to enhance the quality of reconstructed images, a probabilistic undersampling mask optimization with weight adaptation for magnetic resonance imaging (MRI) reconstruction network (PUOR-Net) is proposed based on the undersampling mask optimization strategy and the iterative shrinkage-thresholding algorithm. PUOR-Net comprises two modules: the undersampling subnet and the reconstruction subnet. Within the undersampling subnet, PUOR-Net dynamically generates an optimized undersampling mask through adaptive weight adjustment mechanisms, overcoming the limitations of traditional methods reliant on pre-designed undersampling mask. The reconstruction subnet integrates complex convolution and channel attention mechanisms to further leverage data potential and accurately recover image detail. Experimental results show that PUOR-Net outperforms better on two MRI datasets than the traditional method using the fixed undersampling mask, which verifies the effectiveness of its adaptive optimization strategy. In addition, PUOR-Net demonstrates performance advantages compared to similar optimized mask joint learning networks.