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Abstract #0530

Motion Analysis in Fetal MRI using Deep Pose Estimator

Junshen Xu1, Esra Abaci Turk2, Borjan Gagoski2, Polina Golland3, P. Ellen Grant2,4, and Elfar Adalsteinsson5
1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, 3Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 4Harvard Medical School, Boston, MA, United States, 5Massachusetts Institute of Technology, Cambridge, MA, United States

Fetal motion is an important measure for monitoring fetal health and neurological function. However, current clinical MRI and ultrasound assessments of fetal motion are qualitative and cannot reflect detailed 3D motion of each body part. In this work, we propose a method for fetal motion analysis in MRI using a deep pose estimator. We train a neural network to estimate fetal pose from MR volumes, and extract quantitative metrics of motion from the time series of fetal pose. In the experiments, we study how different conditions affect fetal motion, such as gestational age and maternal position during scan.

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