Improved Statistical Efficiency of Simultaneous Multi-Slice fMRI by Reconstruction with Spatially Adaptive Temporal Smoothing

Abstract We introduce an approach to reconstruction of simultaneous multi-slice (SMS)-fMRI data that improves the statistical efficiency of general linear model (GLM) regression. The method incorporates regularization to adjust temporal smoothness in a spatially varying, encoding-dependent manner, reducing the g-factor noise amplification per temporal degree of freedom. This results in a net improvement in the statistical efficiency of a GLM analysis, where the efficiency gain is derived analytically as a function of the reconstruction parameters. Residual slice leakage and aliasing is limited when fMRI signal energy is dominated by low frequencies. Theoretical, simulated and experimental results demonstrate a marked improvement in GLM efficiency in the temporally regularized reconstructions compared to conventional SENSE and slice-GRAPPA reconstructions, particularly in central brain regions. Furthermore, experimental results confirm that residual slice leakage and aliasing errors are not noticeably increased compared to slice-GRAPPA reconstruction. This approach to temporally regularized image reconstruction in SMS-fMRI improves statistical power, and allows for explicit choice of reconstruction parameters by directly assessing their impact on GLM efficiency.