# SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI arXiv: https://arxiv.org/abs/2206.10802 ยท HTML: https://mlsanity.com/paper/2206.10802/ ## Key facts - Cited by 26 papers in the map (data through 14 Oct 2026). - Builds on 13 of its 32 references that are in the map. - First posted on arXiv 22 Jun 2022. ## Builds on (most cited first, 13 of 13) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016) - [An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset](https://mlsanity.com/paper/2010.15526/) (2020) - [Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast](https://mlsanity.com/paper/2012.13340/) (2020) - [Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration](https://mlsanity.com/paper/1803.05982/) (2018) - [3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images](https://mlsanity.com/paper/1709.06341/) (2017) - [Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry](https://mlsanity.com/paper/1805.01026/) (2018) - [Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging](https://mlsanity.com/paper/1909.11625/) (2019) ## Built on by (most cited first, 26 of 26) - [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023) - [Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI](https://mlsanity.com/paper/2312.03102/) (2023) - [MultiMorph: On-demand Atlas Construction](https://mlsanity.com/paper/2504.00247/) (2025) - [Vision Transformers in Medical Imaging: A Review](https://mlsanity.com/paper/2211.10043/) (2022) - [Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration](https://mlsanity.com/paper/2406.14534/) (2024) - [Fast and Explicit: Slice-to-Volume Reconstruction via 3D Gaussian Primitives with Analytic Point Spread Function Modeling](https://mlsanity.com/paper/2512.11624/) (2025) - [Rigid Single-Slice-in-Volume registration via rotation-equivariant 2D/3D feature matching](https://mlsanity.com/paper/2410.18683/) (2024) - [Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations](https://mlsanity.com/paper/2505.09565/) (2025) - [SpaER: Learning Spatio-temporal Equivariant Representations for Fetal Brain Motion Tracking](https://mlsanity.com/paper/2407.20198/) (2024) - [AVP-AP: Self-supervised Automatic View Positioning in 3D cardiac CT via Atlas Prompting](https://mlsanity.com/paper/2504.05966/) (2025) - [A self-attention model for robust rigid slice-to-volume registration of functional MRI](https://mlsanity.com/paper/2404.04546/) (2024) - [From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction](https://mlsanity.com/paper/2506.16210/) (2025) - [Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping](https://mlsanity.com/paper/2508.10680/) (2025) - [cSVR: Convolutional Slice-to-Volume Reconstruction](https://mlsanity.com/paper/2601.07519/) (2026) - [LayersReg: A Layer-by-Layer Progressive Regressor for Reliable Intraoperative 3D/2D Registration](https://mlsanity.com/paper/2606.26647/) (2026) - [Self-Supervised Isotropic Superresolution Fetal Brain MRI](https://mlsanity.com/paper/2211.06502/) (2022) - [Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model](https://mlsanity.com/paper/2310.10209/) (2023) - [A Literature Review on Fetus Brain Motion Correction in MRI](https://mlsanity.com/paper/2401.16782/) (2024) - [A Unified Deep Learning Framework for Motion Correction in Medical Imaging](https://mlsanity.com/paper/2409.14204/) (2024) - [SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction](https://mlsanity.com/paper/2505.17472/) (2025) - [RealKeyMorph: Keypoints in Real-world Coordinates for Resolution-agnostic Image Registration](https://mlsanity.com/paper/2506.10344/) (2025) - [Equivariant symmetry-aware head pose estimation for fetal MRI](https://mlsanity.com/paper/2512.04890/) (2025) - [Self-Supervised Slice-to-Volume Reconstruction with Gaussian Representations for Fetal MRI](https://mlsanity.com/paper/2601.22990/) (2026) - [PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping](https://mlsanity.com/paper/2607.20136/) (2026) - [SCoPE-Reg: Efficient Rigid Ultrasound Slice-to-Volume Registration via State-Space Correlation and Closed-Form Pose Estimation](https://mlsanity.com/paper/2608.28715/) (2026) - [GraphSVR: q-Space--Aware Graph-Based Slice-to-Volume Registration for Diffusion MRI](https://mlsanity.com/paper/2609.24732/) (2026) ## Abstract (arXiv) Volumetric reconstruction of fetal brains from multiple stacks of MR slices, acquired in the presence of almost unpredictable and often severe subject motion, is a challenging task that is highly sensitive to the initialization of slice-to-volume transformations. We propose a novel slice-to-volume registration method using Transformers trained on synthetically transformed data, which model multiple stacks of MR slices as a sequence. With the attention mechanism, our model automatically detects the relevance between slices and predicts the transformation of one slice using information from other slices. We also estimate the underlying 3D volume to assist slice-to-volume registration and update the volume and transformations alternately to improve accuracy. Results on synthetic data show that our method achieves lower registration error and better reconstruction quality compared with existing state-of-the-art methods. Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.