Abstract #4858

Fetal Motion Prediction from Volumetric MRI using Machine Learning

Junshen of Xu¹, Molin Zhang², Larry Zhang^1,3, Ellen Grant^4,5, Polina Golland^1,3, and Elfar Adalsteinsson^1,6

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

Prospective motion correction is a challenge in clinical fetal MR imaging as fetal motion is erratic and often substantial. To address this problem, we propose a two-stage machine learning pipeline to extract fetal poses from echo planar MRI volumes at previous time points to predict future pose. This pipeline can be used to learn kinematic models of fetal motion and serve as valuable auxiliary information for real-time, online slice prescription in fetal MRI.

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