ArticleAJNR. American journal of neuroradiology2024
MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR Images.
Article in AJNR. American journal of neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- DREAMER: Rapid and Simultaneous Multiple Contrast Magnetic Resonance Imaging of Solid and Soft Tissue.Magnetic resonance in medicine · 2026Article
- Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.Magnetic resonance in medicine · 2025Article
- Craniofacial Imaging of Pediatric Patients by Ultrashort Echo-Time Bone-Selective MRI in Comparison to CT.Academic radiology · 2024Article
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12 authors.
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Abstract
background and purposeCT imaging exposes patients to ionizing radiation. MR imaging is radiation free but previously has not been able to produce diagnostic-quality images of bone on a timeline suitable for clinical use. We developed automated motion correction and use deep learning to generate pseudo-CT images from MR images. We aim to evaluate whether motion-corrected pseudo-CT produces cranial images that have potential to be acceptable for clinical use. MATERIALS AND
methodsPatients younger than age 18 who underwent CT imaging of the head for either trauma or evaluation of cranial suture patency were recruited. Subjects underwent a 5-minute golden-angle stack-of-stars radial volumetric interpolated breath-hold MR image. Motion correction was applied to the MR imaging followed by a deep learning-based method to generate pseudo-CT images. CT and pseudo-CT images were evaluated and, based on indication for imaging, either presence of skull fracture or cranial suture patency was first recorded while viewing the MR imaging-based pseudo-CT and then recorded while viewing the clinical CT.
resultsA total of 12 patients underwent CT and MR imaging to evaluate suture patency, and 60 patients underwent CT and MR imaging for evaluation of head trauma. For cranial suture patency, pseudo-CT had 100% specificity and 100% sensitivity for the identification of suture closure. For identification of skull fractures, pseudo-CT had 100% specificity and 90% sensitivity.
conclusionsOur early results show that automated motion-corrected and deep learning-generated pseudo-CT images of the pediatric skull have potential for clinical use and offer a high level of diagnostic accuracy when compared with standard CT scans.
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