Selection of Image Support Region and of an Improved Regularization for Non-Cartesian SENSE

Yoon Chung Kim¹, Jeffrey Fessler², Douglas Noll¹

Even though non-Cartesian parallel imaging has demonstrated increasing potential for an acquisition tool in MRI, there are still drawbacks such as reduced SNR and incomplete suppression of the undersampling or aliasing artifact. In suppressing such artifacts, the selection of image support, specifying a reconstruction region of interest is an important factor, due to the complex aliasing pattern associated with undersampling. Proper selection of image support can improve the conditioning of the reconstruction by constraining regions that are known to be zero. In this study, we investigate how the selection of image support region affects the performance of non-Cartesian SENSE reconstruction applied to undersampled spiral k-space data. Considering a potential effect of the sharp edges of a conventional mask on aliasing artifact, we also applied a smoothed mask through an additional regularized term to give smoothness to the mask edges. We tested our hypotheses on masking effects with the simulation and in-vivo human data and our results show that using a moderate size of mask can improve the image quality and the smoothing the mask is effective in suppressing aliasing artifact. Functional MRI result also indicates that softening function further increases the number of activated pixels and tSNR, and reduces image domain error.