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Volumetric reconstruction of fetal brains from multiple stacks of MR slices,acquired in the presence of almost unpredictable and often severe subjectmotion, is a challenging task that is highly sensitive to the initialization ofslice-to-volume transformations. We propose a novel slice-to-volumeregistration method using Transformers trained on synthetically transformeddata, which model multiple stacks of MR slices as a sequence. With theattention mechanism, our model automatically detects the relevance betweenslices and predicts the transformation of one slice using information fromother slices. We also estimate the underlying 3D volume to assistslice-to-volume registration and update the volume and transformationsalternately to improve accuracy. Results on synthetic data show that our methodachieves lower registration error and better reconstruction quality comparedwith existing state-of-the-art methods. Experiments with real-world MRI dataare also performed to demonstrate the ability of the proposed model to improvethe quality of 3D reconstruction under severe fetal motion.

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI