Application of Perceptual Difference Model (PDM) on regularization techniques of parallel MR imaging

Parallel magnetic resonance imaging through sensitivity encoding using multiple receiver coils has emerged as an effective tool to reduce imaging time or improve the image quality. Reconstructed image quality is limited by the noise in the acquired k-space data, inaccurate estimation of the sensitivity map, and the ill-conditioned nature of the coefficient matrix. Tikhonov Regularization is currently the most popular method to solve the ill-condition problem. Selections of the regularization map and the regularization parameter are very important. The Perceptual Difference Model (PDM) is a quantitative image quality evaluation tool which has been successfully applied to varieties of MR applications. High correlation between the human rating and the PDM score shows that PDM could be suitable for evaluating image quality in parallel MR imaging. By applying PDM, we compared four methods of selecting the regularization map and four methods of selecting regularization parameter. We find that generalized series (GS) method to select the regularization map together with spatially adaptive method to select the regularization parameter gives the best solution to reconstruct the image. PDM also work as a quantitative image quality index to optimize two important free parameters in spatially adaptive method. We conclude that PDM is an effective tool in helping design and optimize reconstruction methods in parallel MR imaging.