  Motion correction is an essential preprocessing step in functional Magnetic
Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts
caused by fetal movement and maternal breathing and consequently to suppress
erroneous signal correlations. Current motion correction approaches for fetal
fMRI choose a single 3D volume from a specific acquisition timepoint with least
motion artefacts as reference volume, and perform interpolation for the
reconstruction of the motion corrected time series. The results can suffer, if
no low-motion frame is available, and if reconstruction does not exploit any
assumptions about the continuity of the fMRI signal. Here, we propose a novel
framework, which estimates a high-resolution reference volume by using
outlier-robust motion correction, and by utilizing Huber L2 regularization for
intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI.
We performed an extensive parameter study to investigate the effectiveness of
motion estimation and present in this work benchmark metrics to quantify the
effect of motion correction and regularised volumetric reconstruction
approaches on functional connectivity computations. We demonstrate the proposed
framework's ability to improve functional connectivity estimates,
reproducibility and signal interpretability, which is clinically highly
desirable for the establishment of prognostic noninvasive imaging biomarkers.
The motion correction and volumetric reconstruction framework is made available
as an open-source package of NiftyMIC.
