Global and local constrained parallel MRI reconstruction by exploiting dual sparsity and self-consistency

In this study, we introduce global and local constrained reconstruction for highly undersampled magnetic resonance imaging (MRI). This reconstruction not only exploits dual sparsity and self-consistency constraints, but also effectively decouples them regionally and stepwise. Unlike conventional parallel MRI or compressed sensing (CS), we employed multi-level variable-density k-space sampling, wherein the sampling density becomes sparser from the central to the peripheral region (full-shifted lattice R2-M2 undersampling-incoherent pseudo-random undersampling). Furthermore, complementary local k-space model was introduced as a union of wavelet directional filtered signals and its residual for regionally independent reconstruction, in which wavelet subbands are represented in compact frequency partitions. The subbands were partially leaked over neighboring partitions. To further enhance the accuracy of image details and eliminate potential discrepancies resulting from separate regional reconstructions, selfconsistency constrained reconstruction was performed globally over the entire k space. Simulations and experimental studies demonstrated that the proposed technique substantially outperforms conventional methods in suppressing artifacts and noise with increasing acceleration factors.

Automated summary

This study presents a global and local constrained reconstruction for highly undersampled MRI, which effectively decouples dual sparsity and self-consistency constraints, and utilizes multi-level variable-density k-space sampling and complementary local k-space model for enhanced accuracy in image reconstruction. The proposed technique outperforms conventional methods in artifact suppression and noise reduction with increasing acceleration factors.