Abstract #0607

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

Parna Eshraghi Boroojeni¹, Yasheng Chen², Paul K. Commean¹, Cihat Eldeniz¹, Udayabhanu Jammalamadaka¹, Gary B. Skolnick³, Kamlesh B. Patel³, and Hongyu An¹

¹Mallinckrodt Institute of Radiology, Washington University in St. Louis, Saint louis, MO, United States, ²Department of Neurology, Washington University in St. Louis, Saint louis, MO, United States, ³Division of Plastic and Reconstructive Surgery, Washington University in St. Louis, Saint louis, MO, United States

Computed tomography (CT) scans are commonly used in pediatric patients with head trauma and craniosynostosis to identify skull fractures and sutures, respectively. However, the ionizing radiation associated with the CT scans increases the pediatric patients’ risk for cancer. We developed a deep learning-based method, which consists of two networks focusing on skull and head separately, to generate high-resolution pseudo-CT (pCT) from a radial MR scan. A Dice coefficient of 0.90 ± 0.02 was obtained in the bone.Moreover, a pCT mean absolute error (MAE) of 87.5 ± 4.4 HU was achieved.

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