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Abstract #3222

COCONET: A Coordinate-Convolutional patch-based ResUnet for MR Pediatric Image Synthesis

Tongyao Wang1, Yasheng Chen1, Paul K Commean1, Cihat Eldeniz1, Corinne Merrill1, Gary B Skolnick1, Kamlesh B Patel1, and Hongyu An1
1Washington University in St. Louis, St. Louis, MO, United States

Synopsis

Keywords: AI/ML Software, Head & Neck/ENT

Motivation: CT is widely used for detecting pediatric cranial abnormalities but can increase the risk of cancer due to ionizing radiation. MR-synthesized pseudo-CT (pCT) is a safe alternative but challenging for infant data. The generalization of pCT to more than one magnetic field strength is needed for broad clinical adoption.

Goal(s): We aim to develop a method to generate pCTs for children (0-18 years old) using MRI acquired at 1.5T and 3T.

Approach: We proposed a 3D patch-based coordinate-convolutional ResUNet (COCONET) and utilized transfer learning to refine models for infants and different magnetic fields.

Results: Our method produced pCTs similar to the gold-standard CTs.

Impact: This study provides high-resolution pCT images from pediatric MRI imaging. It provides an alternative pediatric cranial bone imaging method free from ionizing radiation.

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Keywords