Abstract #2817

Kim Y, Noll D

In this study, we compare the performance of spiral SENSE and spiral GRAPPA in functional data in terms of SNR in time course, error in image domain compared to fully sampled data and number of activated pixels. Our study also focused on finding reasonable approach to updating calibration data such as sensitivity map or GRAPPA weights in case of minimal or significant head motion. The result shows SENSE has less error in image domain when compared with fully sampled data. Using moving average of calibration data seems to work well in both situation where there is minimal or significant motion.