Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field
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Abstract (arXiv)
Undersampled MRI reconstruction is crucial for accelerating clinical scanning. Dual-domain reconstruction network is performant among SoTA deep learning methods. In this paper, we rethink dual-domain model design from the perspective of the receptive field, which is needed for image recovery and K-space interpolation problems. Further, we introduce domain-specific modules for dual-domain reconstruction, namely k-space global initialization and image-domain parallel local detail enhancement. We evaluate our modules by translating a SoTA method DuDoRNet under different conventions of MRI reconstruction including image-domain, dual-domain, and reference-guided reconstruction on the public IXI dataset. Our model DuDoRNet+ achieves significant improvements over competing deep learning methods.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 24 references that are in the map. First posted on arXiv 19 Mar 2023.
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17 of 24 references.
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