Using the Perceptual Difference Model (PDM) to Optimize GRAPPA Reconstruction

Abstract

Parallel imaging techniques are being applied in MRI to improve the spatial or temporal resolution. Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is one of the most popular reconstruction techniques in parallel imaging. In GRAPPA, several k-space lines are acquired in addition to the normal subsampled data acquisition. Coil mapping information is extracted from these lines and used to reconstruct the missing k-space lines. These additionally acquired k-space lines can also be used in the final reconstruction so as to improve the image quality. In GRAPPA, carefully selecting the calibration region and sampling schemes can greatly reduce the noise and reconstruction artifact and improve the image quality. Perceptual Difference Model (PDM) is a quantitative image quality evaluation tool which has been successfully applied to varieties of MR applications. High correlation between human rating and PDM scores in previous studies shows that PDM is suitable for evaluating image quality in parallel MR imaging. We used PDM to quantitatively compare the quality of images reconstructed with different calibration regions and sampling schemes. We conclude that when the location of the calibration region is set at 0.8 of the phase encoding direction, and the width is set as 20% of total available fitting length, the best reconstruction image could be achieved. One should also set the outer region factor as small as possible. As an example, with all these optimizations, the time used to achieve the same image quality would be reduced by 16% as compared to unoptimized GRAPPA.