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Abstract #4895

Noise-Facilitated GRAPPA Reconstruction for FMRI

Hu Cheng1, Wei Lin2, Feng Huang2

1Indiana University, Bloomington, IN, United States; 2Invivo Diagnostic Imaging, Gainesville, FL, United States

In fMRI, temporal SNR is the main concern in the optimization of parallel imaging algorithms such as GRAPPA. It is shown in this work that adding noise to the auto-calibration signal (ACS) region of GRAPPA data can increase the temporal SNR of fMRI series, with a minimal impact on image quality. Simulation on the EPI images of a phantom and human subject demonstrated that image quality can be improved by adding a certain amount of noise to the raw data of reference scans, while the temporal SNR can be further improved with a higher level of additive ACS noise.