2 research outputs found
Edge-weighted pFISTA-Net for MRI Reconstruction
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