Keywords: Image Reconstruction, Image Reconstruction
Motivation: SENSE-based reconstruction is challenging for clinical imaging when rotating the RRFCA into multiple positions; therefore, a novel calibration-free GRAPPA-based method was developed.
Goal(s): To effectively reconstruct k-space data acquired from the RRFCA, enhancing image quality compared to a conventional stationary array without a scan time penalty.
Approach: Conventional GRAPPA was extended by uncovering a subset of the radial grid to cope with the rotation of the RRFCA. Numerical and human brain images were used for validation.
Results: Image quality was improved using the proposed method. Up to 58% reduction in RMSE and 2.5% increase in SSIM was achieved while maintaining scan time.
Impact: The RRFCA utilising our novel calibration-free, GRAPPA-based, radial image reconstruction method provides a clinically relevant parallel imaging technique. In the future, our approach may incorporate compressed sensing to further reduce motion artifacts, particularly in applications like cardiac and dynamic MRI.
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