Keywords: Other AI/ML, Motion Correction, deep learning, equivariant networks, neuroimaging
Motivation: Fetal brain abnormalities are identified by acquiring stacks of high-resolution 2D
anatomical images along standard directions. However, unpredictable fetal motion
and maternal abnormalities lead to double-oblique slices and coverage gaps, which pose
challenges for radiological assessment.
Goal(s): We aim to estimate fetal head pose from low-resolution 3D EPI navigators interleaved with 2D anatomical images to enable dynamic prescription of the imaging plane.
Approach: We train a 3D rigid-equivariant network to rapidly estimate rigid head pose (translation and rotation).
Results: We report accurate pose estimates on standard-alone EPI volumes, generalization to interleaved navigators, and robustness to large motion.
Impact: Our method demonstrates the promise of fetal head pose estimation and opensnew possibilities for dynamic prescription of the imaging plane that tracks thefetal head in real time.
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