TY - JOUR
T1 - Fast MRI Reconstruction via Edge Attention
AU - Yang , Hanhui
AU - Li , Juncheng
AU - Lui , Lok Ming
AU - Ying , Shihui
AU - Shi , Jun
AU - Zeng , Tieyong
JO - Communications in Computational Physics
VL - 5
SP - 1409
EP - 1431
PY - 2023
DA - 2023/06
SN - 33
DO - http://doi.org/10.4208/cicp.OA-2022-0309
UR - https://global-sci.org/intro/article_detail/cicp/21766.html
KW - Image restoration, MRI reconstruction, Edge attention.
AB - <p style="text-align: justify;">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 ${\rm Q_{image}},$ ${\rm K_{edge}},$ and ${\rm V_{image}},$ respectively. Then EAM
pairs the ${\rm Q_{image}}$ with ${\rm K_{edge}}$ 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.</p>