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arXiv · 2303.10611

Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field

Ziqi Gao·S. Kevin Zhou
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Abstract

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.

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Ziqi Gao, S. Kevin Zhou. 2023-03-19. Rethinking Dual-Domain Undersampled MRI reconstruction: domain-specific design from the perspective of the receptive field. https://arxiv.org/abs/2303.10611

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