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 detail...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.展开更多
基金This work is supported in part by the National Key R&D Program of China under Grant 2021YFE0203700 and 2021YFA1003004in part by the Natural Science Foundation of Shanghai under Grand 23ZR1422200+1 种基金in part by the Shanghai Sailing Program under Grant 23YF1412800in part by the NSFC/RGC N CUHK 415/19,Grant ITF MHP/038/20,Grant CRF 8730063,Grant RGC 14300219,14302920,14301121,and CUHK Direct Grant for Research.
文摘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.
