  Fast and accurate MRI reconstruction is a key concern in modern clinical
practice. Recently, numerous Deep-Learning methods have been proposed for MRI
reconstruction, however, they usually fail to reconstruct sharp details from
the subsampled k-space data. To solve this problem, we propose a lightweight
and accurate Edge Attention MRI Reconstruction Network (EAMRI) to reconstruct
images with edge guidance. Specifically, we design an efficient Edge Prediction
Network to directly predict accurate edges from the blurred image. Meanwhile,
we propose a novel Edge Attention Module (EAM) to guide the image
reconstruction utilizing the extracted edge priors, as inspired by the popular
self-attention mechanism. EAM first projects the input image and edges into
Q_image, K_edge, and V_image, respectively. Then EAM pairs the Q_image with
K_edge along the channel dimension, such that 1) it can search globally for the
high-frequency image features that are activated by the edge priors; 2) the
overall computation burdens are largely reduced compared with the traditional
spatial-wise attention. With the help of EAM, the predicted edge priors can
effectively guide the model to reconstruct high-quality MR images with accurate
edges. Extensive experiments show that our proposed EAMRI outperforms other
methods with fewer parameters and can recover more accurate edges.
