Motion correction based reconstruction method for compressively sampled cardiac MR imaging.

Area of Science:

• Medical Imaging
• Image Reconstruction
• Biomedical Engineering

Background:

• Respiratory motion significantly degrades Magnetic Resonance (MR) image quality, causing blurring artifacts.
• Fast imaging techniques like compressed sensing (CS) exacerbate these artifacts, especially with under-sampled k-space data.
• Existing CS methods for MR image reconstruction require motion correction for optimal performance.

Purpose of the Study:

• To develop a novel compressed sensing (CS) based technique for motion-corrected MR image reconstruction.
• To improve the quality of cardiac cine images acquired with highly under-sampled k-space data.
• To offer a clinically feasible and simpler alternative to existing motion correction methods.

Main Methods:

• K-space data were sorted into respiratory states using frequency domain phase correlation.
• Multiple sparsity constraints were applied for reconstructing cardiac cine images from under-sampled data.
• A demon-based registration technique and a novel reconstruction approach were integrated for motion-free image generation.

Main Results:

• The proposed CS-based motion correction technique demonstrated superior performance compared to standard CS reconstruction for cardiac cine images.
• The method effectively reduced motion-induced blurring artifacts in both simulated and clinical MR data.
• Performance was validated across various acceleration rates, indicating robustness.

Conclusions:

• The novel CS-based motion correction method significantly enhances MR cardiac cine image quality.
• This technique offers a simpler and more practical approach for clinical implementation compared to existing methods.
• The integration of multiple sparsity constraints and advanced registration improves reconstruction from under-sampled, motion-corrupted data.