Fast MRI Reconstruction via Edge Attention (Communications in Computational Physics, 2023)
Fast and accurate MRI reconstruction is a key concern in modern clinicalpractice. Recently, numerous Deep-Learning methods have been proposed for MRIreconstruction, 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 withedge guidance. Specifically, we design an efficient Edge Prediction Network to directlypredict accurate edges from the blurred image. Meanwhile, we propose a novel EdgeAttention Module (EAM) to guide the image reconstruction utilizing the extractededge priors, as inspired by the popular self-attention mechanism. EAM first projectsthe input image and edges into ${\rm Q_{image}},$ ${\rm K_{edge}},$ and ${\rm V_{image}},$ respectively. Then EAMpairs the ${\rm Q_{image}}$ with ${\rm K_{edge}}$ along the channel dimension, such that 1) it can searchglobally for the high-frequency image features that are activated by the edge priors;2) the overall computation burdens are largely reduced compared with the traditionalspatial-wise attention. With the help of EAM, the predicted edge priors can effectivelyguide the model to reconstruct high-quality MR images with accurate edges. Extensiveexperiments show that our proposed EAMRI outperforms other methods with fewerparameters and can recover more accurate edges.
Keywords: Image restoration; MRI reconstruction; Edge attention.