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bioRxiv · 10.1101/646554

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

Chiew, M.·Miller, K. L.
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Abstract

We introduce an approach to reconstruction of simultaneous multi-slice (SMS)-fMRI data that improves statistical efficiency. 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 tSNR and GLM efficiency, where the efficiency gain can be derived analytically as a function of the encoding and reconstruction parameters. Residual slice leakage and aliasing is limited when fMRI signal energy is dominated by low frequencies. Analytical predictions, simulated and experimental results demonstrate a marked improvement in statistical efficiency in the temporally regularized reconstructions compared to conventional 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 noise variance per degree of freedom.

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Chiew, M., Miller, K. L.. 2019-05-24. Improved Statistical Efficiency of Simultaneous Multi-Slice fMRI by Reconstruction with Spatially Adaptive Temporal Smoothing. https://doi.org/10.1101/646554

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