  Multi-slice magnetic resonance images of the fetal brain are usually
contaminated by severe and arbitrary fetal and maternal motion. Hence, stable
and robust motion correction is necessary to reconstruct high-resolution 3D
fetal brain volume for clinical diagnosis and quantitative analysis. However,
the conventional registration-based correction has a limited capture range and
is insufficient for detecting relatively large motions. Here, we present a
novel Affinity Fusion-based Framework for Iteratively Random Motion (AFFIRM)
correction of the multi-slice fetal brain MRI. It learns the sequential motion
from multiple stacks of slices and integrates the features between 2D slices
and reconstructed 3D volume using affinity fusion, which resembles the
iterations between slice-to-volume registration and volumetric reconstruction
in the regular pipeline. The method accurately estimates the motion regardless
of brain orientations and outperforms other state-of-the-art learning-based
methods on the simulated motion-corrupted data, with a 48.4% reduction of mean
absolute error for rotation and 61.3% for displacement. We then incorporated
AFFIRM into the multi-resolution slice-to-volume registration and tested it on
the real-world fetal MRI scans at different gestation stages. The results
indicated that adding AFFIRM to the conventional pipeline improved the success
rate of fetal brain super-resolution reconstruction from 77.2% to 91.9%.
