Abstract #4807

SVoRT: Slice-to-volume Registration for Fetal Brain MRI Reconstruction with Transformers

Junshen Xu¹, Daniel Moyer², P. Ellen Grant^3,4,5,6, Polina Golland^1,2, Juan Eugenio Iglesias^2,4,7,8, and Elfar Adalsteinsson^1,9

¹Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ²Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ⁴Harvard Medical School, Boston, MA, United States, ⁵Department of Pediatrics, Boston Children’s Hospital, Boston, MA, United States, ⁶Department of Radiology, Boston Children’s Hospital, Boston, MA, United States, ⁷Centre for Medical Image Computing, University College London, London, United Kingdom, ⁸Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Cambridge, MA, United States, ⁹Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States

Synopsis

Volumetric reconstruction of fetal brains from MR slices is a challenging task, which is sensitive to the initialization of slice-to-volume transformations. Further complicating the task is the unpredictable fetal motion. In this abstract, we proposed a novel method for slice-to-volume registration using transformers, which models the stacks of MR slices as a sequence. With the attention mechanism, the proposed model predicts the transformation of one slice using information from other slices. Results show that the proposed method achieves not only lower registration error but also better generalizability compared with other state-of-the-art methods for slice-to-volume registration of fetal MRI.

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