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Abstract #0174

Deep learning-based high-resolution pseudo-CT to detect cranial bone abnormalities for pediatric patients using MRI

Parna Eshraghi Boroojeni1, Yasheng Chen1, Cihat Eldeniz1, Paul Commean1, Gary Skolnick1, Kamlesh Patel1, and Hongyu An1
1Washington University in Saint Louis, Saint Louis, MO, United States


Head trauma is common in the pediatric population. Craniosynostosis is abnormal early fusion of a cranial suture, causing an irregular-shaped cranium.3D high-resolution head CT scans are commonly used in these pediatric patients to identify skull fractures and sutures. However, CT exposespediatric patients to ionizing radiationand increases risk of cancer. We developed a robust and automated deep learning method to convert MR images to pseudo-CT (pCT) that can facilitate translating MR cranial bone imaging into clinical practice. An average Dice Coefficient of 0.89 and mean absolute error of 72.45 HU between pCT and CT were achieved.

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