Dual-Branch Deep Unfolding Network for Compressed Sensing MRI Reconstruction

In the field of compressed sensing magnetic resonance imaging (CS-MRI), deep unfolding networks (DUNs) achieve high interpretability and superior performance. However, existing DUN-based methods often treat different components of the MR image uniformly without considering their respective unique characteristics, leading to insufficient detail capture and suboptimal performance. To address this issue, we propose a Dual-BrancH Deep Unfolding Network (DBH-Net), which employs parallel under-complete (UC) and over-complete (OC) branches to alternately reconstruct different components from the under-sampled MR image. The UC branch focuses on extracting low-frequency features by expanding the receptive field, while the OC branch emphasizes high-frequency features by restricting the receptive field. Besides the independent descriptive abilities of dual-branch, the unique characteristics of DUN facilitate a tighter integration between the two branches. Additionally, we introduce an Auxiliary Information Fusion Block (AIFB) to transfer multi-channel auxiliary information between stages, effectively reducing information loss. Extensive experiments on three datasets demonstrate that our proposed DBH-Net outperforms existing state-of-the-art methods.