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Undersampled MRI reconstruction is crucial for accelerating clinical scanningprocedures. Recent deep learning methods for MRI reconstruction adopt CNN orViT as backbone, which lack in utilizing the complementary properties of CNNand ViT. In this paper, we propose DuDoRNeXt, whose backbone hybridizes CNN andViT in an domain-specific, intra-stage way. Besides our hybrid vertical layoutdesign, we introduce domain-specific modules for dual-domain reconstruction,namely image-domain parallel local detail enhancement and k-space globalinitialization. We evaluate different conventions of MRI reconstructionincluding image-domain, k-space-domain, and dual-domain reconstruction with areference protocol on the IXI dataset and an in-house multi-contrast dataset.DuDoRNeXt achieves significant improvements over competing deep learningmethods.

DuDoRNeXt: A hybrid model for dual-domain undersampled MRI reconstruction