SSCA-DIPNet: Structured Sparse and Channel-Attentive Deep Image Prior Network for Highly-Accelerated MRI

MRI aids diagnosis yet suffers slow scanning. k-space undersampling causes ill-posed reconstruction, with existing methods flawed. This study builds a data-efficient network to tackle high-acceleration, limited-data MRI reconstruction. We proposed the Structured Sparse and Channel-Attentive Deep Image Prior Network (SSCA-DIPNet), which integrated three innovative modules based on ISTA-Net+: (1) a channel-adaptive structured sparsity module for differential feature preservation, (2) a channel-attention deep image prior module for artifact repair, (3) a globally learnable fusion mechanism combined with symmetric loss to enhance model generalization. Two branch models were derived from ISTA-Net+ baseline: SS-Net with only the structured sparsity module, and CA-DIPNet with only the channel-attention deep image prior module. Experiments on two clinical datasets (179 training samples each) and supplementary fastMRI raw k-space validation are performed under 5×/10×/20× acceleration and evaluated via PSNR and SSIM. The proposed model consistently outperforms ISTA-Net+, SS-Net and CA-DIPNet across all scenarios. Using scarce data, SSCA-DIPNet yields robust reconstruction with refined anatomical details and reduced artifacts. It balances reconstruction performance and data efficiency, offering a fresh paradigm for iterative-unfolding MRI reconstruction to support clinical fast scanning under limited data.