Dynamic MR Imaging using generalized series

Dynamic MR Imaging can be achieved by sampling only selected regions in k-space. Optimal region selection in the 3D k-space needs to satisfy “maximum energy compaction” and “minimum acquisition” requirements. In this paper, a novel star-shaped truncation window is proposed to increase the achievable acceleration factor. The proposed window monotonically cuts down the acquisition of the number of k-space samples with lesser energy. The truncation window samples data within a star-shaped region centered around the origin in the ky-kz plane. The proposed window is experimentally found to capture more k-space energy compared to the other existing windows, for the same fraction of kspace acquisition. The missing values are extrapolated using generalized series modeling-based methods. The proposed method is applied to several real and synthetic data sets. The superior performance of the proposed method is illustrated using the standard measures of error images and uptake curve comparisons. Average values of slope error in estimating the enhancement curve are obtained over 5 real data sets of breast and abdomen images, for an acceleration factor of 8. The proposed method results in a slope error of 5%, while the values obtained using rectangular and elliptical windows are 12% and 10%, respectively.