Abstract #1027

Deep Learning-based Fetal-Uterine Motion Modeling from Volumetric EPI Time Series

Muheng Li¹, Yi Xiao¹, Tingyin Liu², Junshen Xu³, Esra Turk⁴, Borjan Gagoski^4,5, Karen Ying¹, Polina Golland^2,3, P. Ellen Grant^4,5, and Elfar Adalsteinsson^3,6

¹Department of Engineering Physics, Tsinghua University, Beijing, China, ²Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, United States, ³Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ⁵Harvard Medical School, Boston, MA, United States, ⁶Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States

We propose a three-dimensional convolutional neural network applied to echo planar EPI time series of pregnant women for the automatic segmentation of the uterus (placenta excluded) and fetal body. The segmentation results are utilized to create a dynamic model for the fetus for retrospective analyses. The 3D dynamic fetal-uterine motion model will provide quantitative information of fetal motion characteristics for diagnostic purposes and may guide future fetal imaging strategies where adaptive, online slice prescription is used to mitigate motion artifacts.

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