  Fetal MRI is heavily constrained by unpredictable and substantial fetal
motion that causes image artifacts and limits the set of viable diagnostic
image contrasts. Current mitigation of motion artifacts is predominantly
performed by fast, single-shot MRI and retrospective motion correction.
Estimation of fetal pose in real time during MRI stands to benefit prospective
methods to detect and mitigate fetal motion artifacts where inferred fetal
motion is combined with online slice prescription with low-latency decision
making. Current developments of deep reinforcement learning (DRL), offer a
novel approach for fetal landmarks detection. In this task 15 agents are
deployed to detect 15 landmarks simultaneously by DRL. The optimization is
challenging, and here we propose an improved DRL that incorporates priors on
physical structure of the fetal body. First, we use graph communication layers
to improve the communication among agents based on a graph where each node
represents a fetal-body landmark. Further, additional reward based on the
distance between agents and physical structures such as the fetal limbs is used
to fully exploit physical structure. Evaluation of this method on a repository
of 3-mm resolution in vivo data demonstrates a mean accuracy of landmark
estimation within 10 mm of ground truth as 87.3%, and a mean error of 6.9 mm.
The proposed DRL for fetal pose landmark search demonstrates a potential
clinical utility for online detection of fetal motion that guides real-time
mitigation of motion artifacts as well as health diagnosis during MRI of the
pregnant mother.
