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Abstract #1220

Retrospectively Gated UTE MRI with Deep-Learning Segmentation to Quantify Preclinical Lung Fibrosis Severity

Ian R Stecker1,2, Sneha Sitaraman3, Matt S Freeman2, Jinbang Guo2, Emily P Martin4, Chase Hall5, Tim E Weaver4, and Zackary I Cleveland1,2

1Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States, 2Center for Pulmonary Imaging, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 3Molecular & Developmental Biology Graduate Program, Cincinnati, OH, United States, 4Division of Neonatology and Pulmonary Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States, 5Division of Pulmonary and Critical Care, University of Kansas Medical Center, Kansas City, KS, United States

Fibrosis contributes to morbidity and mortality in an array of pulmonary diseases, and no therapies exist to halt or reverse its progression. This paucity of treatment strategies results from poorly understood etiology, making it essential to develop animal models that fully mimic human disease and identify noninvasive, quantitative markers for fibrosis severity for use in animal models and patients. Here we report an image reconstruction and analysis pipeline, combining retrospectively gated ultrashort echo time (UTE) MRI and deep-learning segmentation to quantify lung fibrosis progression in Surfactant Protein C (SP-C) knock-in models based on mutations that predispose humans to lung fibrosis.

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