GRAPPA operator for wider radial bands (GROWL) with optimally regularized self-calibration

Area of Science:

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

Background:

• Azimuthally undersampled radial datasets in MRI present reconstruction challenges.
• Existing parallel imaging techniques may have limitations in speed and applicability.

Purpose of the Study:

• To develop and evaluate a novel self-calibrated parallel imaging reconstruction method for azimuthally undersampled radial MRI data.
• To improve reconstruction speed and flexibility compared to existing methods.

Main Methods:

• Utilized a generalized auto-calibrating partially parallel acquisition (GRAPPA) operator to expand radial views.
• Implemented self-calibration using the central k-space region on a rotated Cartesian grid.
• Introduced Tikhonov regularization to optimize the calibration process.

Main Results:

• The proposed method, GRAPPA operator for wider radial bands (GROWL), was successfully applied to phantom and in vivo datasets.
• GROWL demonstrated a significant speed advantage due to calibration on the fully sampled k-space center.
• The technique showed applicability to arbitrary-view angle ordering schemes.

Conclusions:

• GROWL offers an efficient and versatile solution for reconstructing azimuthally undersampled radial MRI data.
• The method enhances parallel imaging reconstruction by leveraging GRAPPA operators and self-calibration.
• GROWL represents a valuable advancement for accelerated MRI acquisition and reconstruction.