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.

翻译：欠采样MRI重建对于加速临床扫描至关重要。双域重建网络在当下最先进的深度学习方法中表现优异。本文从感受野视角重新审视双域模型设计——该视角对于图像恢复和K空间插值问题均不可或缺。进一步地，我们引入了专用于双域重建的领域特异性模块，即K空间全局初始化模块与图像域并行局部细节增强模块。通过将现有最先进方法DuDoRNet迁移至不同MRI重建范式（包括图像域重建、双域重建及参考引导重建），我们在公开IXI数据集上评估了所提模块。我们的模型DuDoRNet+相较于竞争性深度学习方法取得了显著性能提升。