Artifact and noise suppression in GRAPPA imaging using improved k-space coil calibration and variable density...

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Medical Imaging
• Image Reconstruction

Background:

• GeneRalized Auto-calibrating Partially Parallel Acquisitions (GRAPPA) is a parallel imaging technique used to enhance MRI resolution.
• Conventional GRAPPA coil calibration uses central k-space signals, which have large amplitude variations, while reconstruction uses outer k-space signals with smaller variations.
• These signal variations can lead to residual image artifacts and noise.

Purpose of the Study:

• To improve GRAPPA coil calibration and variable density (VD) sampling.
• To suppress residual artifacts and noise in GRAPPA reconstructions.
• To optimize sampling and calibration parameters for enhanced MRI performance.

Main Methods:

• Developed a localized coil calibration method in k-space along both phase and frequency encoding directions.
• Acquired outer k-space data using two different reduction factors.
• Reconstructed phantom and in vivo MRI data using both conventional and improved GRAPPA techniques at an acceleration of two.

Main Results:

• The proposed localized coil calibration and VD sampling scheme demonstrated improved GRAPPA performance.
• Optimal sampling and calibration parameters were determined for an acceleration of two.
• Residual artifacts and noise were suppressed compared to conventional GRAPPA.

Conclusions:

• Localized coil calibration and VD sampling significantly enhance GRAPPA's ability to suppress artifacts and noise.
• The optimized GRAPPA technique offers improved image quality in MRI.
• This method provides a more robust approach to parallel MRI reconstruction.