Hu Cheng1. 1. Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405, USA. hucheng@indiana.edu
Abstract
PURPOSE: To investigate the noise variation in multi-run functional MRI (fMRI) scans using generalized autocalibrating partially parallel acquisition (GRAPPA), with a focus on the cause of this variation. MATERIALS AND METHODS: A phantom was continuously scanned for 10 runs using echo-planar imaging (EPI) combined with GRAPPA to simulate a multi-run fMRI exam. The variation of noise between runs was examined for different GRAPPA acceleration factors. The noise variation was also evaluated in a real fMRI experiment with human subjects at an acceleration factor of two. The cause of noise variation was explored by offline reconstruction using different GRAPPA weights and numerical simulation of GRAPPA reference scans. RESULTS: It was found that the noise distribution in the image is stable within a run but may vary randomly from run to run. The variation of noise was also observed in fMRI experiments with human subjects. The variation can be significantly reduced if all the images from individual runs are reconstructed using the same reference scan data. CONCLUSION: Both phantom experiments and human data showed that the noise pattern may change in different fMRI runs. The variation is mainly caused by the random noise in separate reference scans for GRAPPA in each run.
PURPOSE: To investigate the noise variation in multi-run functional MRI (fMRI) scans using generalized autocalibrating partially parallel acquisition (GRAPPA), with a focus on the cause of this variation. MATERIALS AND METHODS: A phantom was continuously scanned for 10 runs using echo-planar imaging (EPI) combined with GRAPPA to simulate a multi-run fMRI exam. The variation of noise between runs was examined for different GRAPPA acceleration factors. The noise variation was also evaluated in a real fMRI experiment with human subjects at an acceleration factor of two. The cause of noise variation was explored by offline reconstruction using different GRAPPA weights and numerical simulation of GRAPPA reference scans. RESULTS: It was found that the noise distribution in the image is stable within a run but may vary randomly from run to run. The variation of noise was also observed in fMRI experiments with human subjects. The variation can be significantly reduced if all the images from individual runs are reconstructed using the same reference scan data. CONCLUSION: Both phantom experiments and human data showed that the noise pattern may change in different fMRI runs. The variation is mainly caused by the random noise in separate reference scans for GRAPPA in each run.