An Algorithm Combining Analysis-based Blind Compressed Sensing and Nonlocal Low-rank Constraints for MRI...

Area of Science:

• Medical Imaging
• Signal Processing
• Computational Science

Background:

• Compressive sensing (CS) reduces data acquisition time in MRI by exploiting image sparsity.
• CS is widely applied in magnetic resonance imaging (MRI) reconstruction to shorten scan durations.

Purpose of the Study:

• To enhance MRI reconstruction by combining analysis-based blind compressed sensing (BCS) with a nonlocal low-rank constraint.
• To achieve superior MR image quality using significantly fewer measurements.

Main Methods:

• The study focuses on analysis-based BCS, integrating a nonlocal low-rank constraint.
• Non-convex Schatten p-functionals are utilized for rank approximation, replacing the conventional nuclear norm.

Main Results:

• The proposed approach demonstrated superior performance compared to existing state-of-the-art algorithms in simulations.
• The method effectively preserves image details while reconstructing from highly undersampled data.

Conclusions:

• The developed method offers a promising advancement in MRI reconstruction, enabling faster scans with high-fidelity images.
• Combining BCS with nonlocal low-rank properties and non-convex optimization provides significant benefits for MRI applications.