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Fetal MRI is heavily constrained by unpredictable and substantial fetalmotion that causes image artifacts and limits the set of viable diagnosticimage contrasts. Current mitigation of motion artifacts is predominantlyperformed by fast, single-shot MRI and retrospective motion correction.Estimation of fetal pose in real time during MRI stands to benefit prospectivemethods to detect and mitigate fetal motion artifacts where inferred fetalmotion is combined with online slice prescription with low-latency decisionmaking. Current developments of deep reinforcement learning (DRL), offer anovel approach for fetal landmarks detection. In this task 15 agents aredeployed to detect 15 landmarks simultaneously by DRL. The optimization ischallenging, and here we propose an improved DRL that incorporates priors onphysical structure of the fetal body. First, we use graph communication layersto improve the communication among agents based on a graph where each noderepresents a fetal-body landmark. Further, additional reward based on thedistance between agents and physical structures such as the fetal limbs is usedto fully exploit physical structure. Evaluation of this method on a repositoryof 3-mm resolution in vivo data demonstrates a mean accuracy of landmarkestimation 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 potentialclinical utility for online detection of fetal motion that guides real-timemitigation of motion artifacts as well as health diagnosis during MRI of thepregnant mother.

Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy