  Accurate characterization of in utero human brain maturation is critical as
it involves complex and interconnected structural and functional processes that
may influence health later in life. Magnetic resonance imaging is a powerful
tool to investigate equivocal neurological patterns during fetal development.
However, the number of acquisitions of satisfactory quality available in this
cohort of sensitive subjects remains scarce, thus hindering the validation of
advanced image processing techniques. Numerical phantoms can mitigate these
limitations by providing a controlled environment with a known ground truth. In
this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance
Acquisition Numerical phantom that simulates clinical T2-weighted fast spin
echo sequences of the fetal brain. This unique tool is based on a general,
flexible and realistic setup that includes stochastic fetal movements, thus
providing images of the fetal brain throughout maturation comparable to
clinical acquisitions. We demonstrate its value to evaluate the robustness and
optimize the accuracy of an algorithm for super-resolution fetal brain magnetic
resonance imaging from simulated motion-corrupted 2D low-resolution series as
compared to a synthetic high-resolution reference volume. We also show that the
images generated can complement clinical datasets to support data-intensive
deep learning methods for fetal brain tissue segmentation.
