ArticleIEEE transactions on medical imaging2020
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Fetal brain MRI atlases and datasets: A review.NeuroImage · 2024Pooled it
- Application of artificial intelligence in paediatric oncology imaging.Pediatric radiology · 2026Review
- AI in Childbirth and Emerging Alternatives to Traditional Fertility Treatments.Reproductive sciences (Thousand Oaks, Calif.) · 2026Review
- Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correction.ArXiv · 2025Article
- Variable Flip Angle Optimization for Fetal Brain Imaging With Reduced Specific Absorption Rate.NMR in biomedicine · 2025Article
- Current Diagnostic, Counseling, and Treatment Options in Non-Severe and Severe Apparently Isolated Fetal Ventriculomegaly.Biomedicines · 2024Review
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Real-time fetal brain tracking for functional fetal MRI.Magnetic resonance in medicine · 2023Article
- Fetal MRI: what's new? A short review.European radiology experimental · 2023Review
- Automatic Ventriculomegaly Detection in Fetal Brain MRI: A Step-by-Step Deep Learning Model for Novel 2D-3D Linear Measurements.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence applied to fetal MRI: A scoping review of current research.The British journal of radiology · 2023Article
- Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging.World journal of clinical cases · 2023Review
- GEOMETRIC CONSTRAINED DEEP LEARNING FOR MOTION CORRECTION OF FETAL BRAIN MR IMAGES.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T.Magnetic resonance in medicine · 2022Article
- Fetal Neuroimaging Updates.Magnetic resonance imaging clinics of North America · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help build automatic MRI motion tracking and navigation systems to overcome the limitations of the current process and improve fetal imaging, we have developed a new real-time image-based motion tracking method based on deep learning that learns to predict fetal motion directly from acquired images. Our method is based on a recurrent neural network, composed of spatial and temporal encoder-decoders, that infers motion parameters from anatomical features extracted from sequences of acquired slices. We compared our trained network on held-out test sets (including data with different characteristics, e.g. different fetuses scanned at different ages, and motion trajectories recorded from volunteer subjects) with networks designed for estimation as well as methods adopted to make predictions. The results show that our method outperformed alternative techniques, and achieved real-time performance with average errors of 3.5 and 8 degrees for the estimation and prediction tasks, respectively. Our real-time deep predictive motion tracking technique can be used to assess fetal movements, to guide slice acquisitions, and to build navigation systems for fetal MRI.
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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.