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Abstract #0082

A Motion-Robust Slice-to-Volume Reconstruction Framework for Fetal Brain MRI

Junshen Xu1, Daniel Moyer2, Borjan Gagoski3, P. Ellen Grant3,4, Polina Golland5, Juan Eugenio Iglesias4,5,6,7, and Elfar Adalsteinsson1
1Massachusetts Institute of Technology, Cambridge, MA, United States, 2Computer Science, Vanderbilt University, Nashville, TN, United States, 3Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, 4Harvard Medical School, Boston, MA, United States, 5Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, 6Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 7Athinoula A. Martinos Center for Biomedical Imaging, Cambridge, MA, United States

Synopsis

Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceVolumetric reconstruction of fetal brains from multiple stacks of MR slices is challenging due to severe subject motion and image artifacts. We propose a deep learning method to solve the slice-to-volume reconstruction problem in two stages. First, a Transformer network is used to correct motion between slices by registering the input slices to a 3D canonical space. Second, an implicit neural network reconstructs the 3D volume by learning a continuous 3D representation of the fetal brain from the 2D observations. Results show that our method achieves high reconstruction quality and outperforms existing state-of-the-art methods in presence of severe fetal motion.

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