ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2022
SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 38 citations in OpenAlex.
- Fully automated deep learning model for the evaluation of cavum septum pellucidum development in normal fetuses using magnetic resonance imaging: a Chinese cohort study.Quantitative imaging in medicine and surgery · 2025Article
- MultiMorph: On-demand Atlas Construction.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2025Article
- Towards automatic US-MR fetal brain image registration with learning-based methods.NeuroImage · 2025Article
- Scanner-based real-time three-dimensional brain + body slice-to-volume reconstruction for T2-weighted 0.55-T low-field fetal magnetic resonance imaging.Pediatric radiology · 2025Article
- Advances in Fetal Brain Imaging.Magnetic resonance imaging clinics of North America · 2024Review
- Calculating Volume of Pig Point Cloud Based on Improved Poisson Reconstruction.Animals : an open access journal from MDPI · 2024Article
- Fetal-BET: Brain Extraction Tool for Fetal MRI.IEEE open journal of engineering in medicine and biology · 2024Article
- SMDB: a Spatial Multimodal Data Browser.Nucleic acids research · 2023Article
- NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI.IEEE transactions on medical imaging · 2023Article
- GEOMETRIC CONSTRAINED DEEP LEARNING FOR MOTION CORRECTION OF FETAL BRAIN MR IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
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.
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Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.