ArticleMagnetic resonance in medicine2022
Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Consistent explainable image quality assessment for medical imaging.Health information science and systems · 2026Article
- Age-Specific Contrast Optimization of bSSFP in Fetal Brain.Magnetic resonance in medicine · 2026Article
- Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Scanner-based real-time automated volumetry reporting of the fetus, amniotic fluid, placenta, and umbilical cord for fetal MRI at 0.55T.Magnetic resonance in medicine · 2026Article
- Variable Flip Angle Optimization for Fetal Brain Imaging With Reduced Specific Absorption Rate.NMR in biomedicine · 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
- Transforming Medical Imaging: The Role of Artificial Intelligence Integration in PACS for Enhanced Diagnostic Accuracy and Workflow EfficiencyCurrent medical imaging · 2025Review
- Current Diagnostic, Counseling, and Treatment Options in Non-Severe and Severe Apparently Isolated Fetal Ventriculomegaly.Biomedicines · 2024Review
- SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI.IEEE transactions on medical imaging · 2024Article
- Recent trends in AI applications for pelvic MRI: a comprehensive review.La Radiologia medica · 2024Review
- MR software tools for real-time decision making and FOV prescription.Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition · 2024Article
- Stochastic-offset-enhanced restricted slice excitation and 180° refocusing designs with spatially non-linear ΔBMagnetic resonance in medicine · 2023Article
- Real-time fetal brain tracking for functional fetal MRI.Magnetic resonance in medicine · 2023Article
- Automatic Localization of the Pons and Vermis on Fetal Brain MR Imaging Using a U-Net Deep Learning Model.AJNR. American journal of neuroradiology · 2023Article
- 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
- NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI.IEEE transactions on medical imaging · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
purposeFetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction.
methodsA convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired.
resultsThe closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899.
conclusionThe proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.
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