# Deformable Slice-to-Volume Registration for Motion Correction in Fetal Body MRI

arXiv: https://arxiv.org/abs/1906.08827 · HTML: https://mlsanity.com/paper/1906.08827/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI](https://mlsanity.com/paper/2312.03102/) (2023)
- [Fetal-BET: Brain Extraction Tool for Fetal MRI](https://mlsanity.com/paper/2310.01523/) (2023)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised Learning](https://mlsanity.com/paper/2106.12407/) (2021)
- [AFFIRM: Affinity Fusion-based Framework for Iteratively Random Motion correction of multi-slice fetal brain MRI](https://mlsanity.com/paper/2205.05851/) (2022)
- [Automatic Segmentation of the Placenta in BOLD MRI Time Series](https://mlsanity.com/paper/2208.02895/) (2022)
- [HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI](https://mlsanity.com/paper/2406.20042/) (2024)
- [SIMPLE: Simultaneous Multi-Plane Self-Supervised Learning for Isotropic MRI Restoration from Anisotropic Data](https://mlsanity.com/paper/2408.13065/) (2024)
- [Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge](https://mlsanity.com/paper/2505.02784/) (2025)
- [Meta-learning Slice-to-Volume Reconstruction in Fetal Brain MRI using Implicit Neural Representations](https://mlsanity.com/paper/2505.09565/) (2025)
- [Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI](https://mlsanity.com/paper/2205.01675/) (2022)
- [OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms](https://mlsanity.com/paper/2208.05616/) (2022)
- [Towards contrast- and pathology-agnostic clinical fetal brain MRI segmentation using SynthSeg](https://mlsanity.com/paper/2504.10244/) (2025)
- [From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstruction](https://mlsanity.com/paper/2506.16210/) (2025)
- [Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction](https://mlsanity.com/paper/2111.00102/) (2021)
- [Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint](https://mlsanity.com/paper/2112.02249/) (2021)
- [Self-supervised Fetal MRI 3D Reconstruction Based on Radiation Diffusion Generation Model](https://mlsanity.com/paper/2310.10209/) (2023)
- [Multi-task learning for joint weakly-supervised segmentation and aortic arch anomaly classification in fetal cardiac MRI](https://mlsanity.com/paper/2311.07234/) (2023)
- [Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series](https://mlsanity.com/paper/2312.05148/) (2023)
- [A Unified Deep Learning Framework for Motion Correction in Medical Imaging](https://mlsanity.com/paper/2409.14204/) (2024)
- [Predicting gestational age at birth in the context of preterm birth from multi-modal fetal MRI](https://mlsanity.com/paper/2606.20172/) (2026)
- [SPARC: Slice-to-volume Pipeline for Automated Reconstruction of gated 3D+time fetal Cardiac MRI](https://mlsanity.com/paper/2608.18616/) (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)

## Abstract (arXiv)

In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28-31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20-34 weeks GA range.