ArticleMagnetic resonance in medicine2025
Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.
Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Phase-Pole-Free Images and Smooth Coil Sensitivity Maps by Regularized Nonlinear Inversion.Magnetic resonance in medicine · 2026Article
- Magnetic Resonance Imaging-Based Cohesive Extended Finite Element Modeling of Atypical Femoral Fracture.Journal of biomechanical engineering · 2026Article
- DREAMER: Rapid and Simultaneous Multiple Contrast Magnetic Resonance Imaging of Solid and Soft Tissue.Magnetic resonance in medicine · 2026Article
- Triplanar Point Cloud Reconstruction of Head Skin Surface from Computed Tomography Images in Markerless Image-Guided Surgery.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
purposeUltrashort echo time (UTE) MRI can be a radiation-free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone-specific MR imaging is limited by long scan times, relatively low spatial resolution, and a time-consuming bone segmentation workflow.
methodsA rapid, high-resolution UTE technique for brain and skull imaging in conjunction with an automatic segmentation pipeline was developed. A dual-RF, dual-echo UTE sequence was optimized for rapid scan time (3 min) and smaller voxel size (0.65 mm
resultsThe weighted least-squares method produces images with enhanced bone specificity, suppression of soft tissue, and separation from air at the sinuses when validated against CT in pediatric patients. Dice similarity coefficient overlap was 0.86 ± 0.05, and the 95th percentile Hausdorff distance was 1.77 ± 0.49 mm between the full-skull binary masks of the optimized UTE and CT in the testing dataset.
conclusionAn optimized MRI acquisition, reconstruction, and segmentation workflow for craniofacial imaging was developed.
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