PFONet:A progressive focus-oriented dual-domain reconstruction network for accelerated MRI
Undersampling k-space data to accelerate Magnetic Resonance Imaging(MRI)is effective but challenging for accurate image reconstruction.Recently,neural networks,particularly dual-domain models that leverage both frequency and image domain data,have gained attention for enhancing MRI reconstruction.However,these methods are inadequate for two main reasons.Firstly,in the frequency domain,these meth-ods with traditional normalization modules treat measurements and zero-filled areas equally,leading to feature shift and suboptimal reconstruction.Secondly,in the image domain,existing methods typically ignore multi-scale features and lack dynamic prosperity,thus being challenging for networks to learn sufficient global-local information to preserve structural details.Here,the authors present a novel progressive focusoriented dual-domain reconstruction network(PFONet)to overcome these limitations in frequency and image domains,re-spectively.In the frequency domain,the authors propose an area normalization module that focuses on the zero-filled areas,progressively mitigates the feature shift,and predicts reliable k-space data.In the image do-main,the authors propose a dynamic attention module with a channel-wise gating mechanism to focus on rich global-local feature extraction from multi-scale receptive fields for detail recovery.Quantitative and qualitative experiments demonstrate that our PFONet achieves competitive performance compared to several state-of-the-art methods while the proposed network is lightweight.
Magnetic Resonance Imaging(MRI)MRI reconstructionProgressive focus-orientedMulti-scale feature aggregation
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