Deep learning based on unrolled algorithm has served as an effective method for accelerated magnetic resonance imaging (MRI). However, many methods ignore the direct use of edge information to assist MRI reconstruction. In this work, we present the edge-weighted pFISTA-Net that directly applies the detected edge map to the soft-thresholding part of pFISTA-Net. The soft-thresholding value of different regions will be adjusted according to the edge map. Experimental results of a public brain dataset show that the proposed yields reconstructions with lower error and better artifact suppression compared with the state-of-the-art deep learning-based methods. The edge-weighted pFISTA-Net also shows robustness for different undersampling masks and edge detection operators. In addition, we extend the edge weighted structure to joint reconstruction and segmentation network and obtain improved reconstruction performance and more accurate segmentation results.

翻译：基于展开算法的深度学习已成为加速磁共振成像的有效方法。然而，许多方法忽略了直接利用边缘信息辅助MRI重建。本文提出基于边缘权重的pFISTA-Net，该方法将检测到的边缘图直接应用于pFISTA-Net的软阈值部分，根据不同区域的边缘图调整软阈值大小。在公开脑部数据集上的实验结果表明，与当前最先进的深度学习方法相比，所提方法可获得更低的重建误差和更好的伪影抑制效果。该网络对不同欠采样掩膜和边缘检测算子均表现出鲁棒性。此外，我们将边缘加权结构扩展至联合重建与分割网络，在提升重建性能的同时获得了更精确的分割结果。