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Abstract #2284

Automatic Segmentation of Hyperpolarized Gas MRI via Deep Learning

Joshua R Astley1,2, Alberto M Biancardi1, Paul JC Hughes1, Laurie J Smith1, Helen Marshall1, Grace T Mussell1, James Eaden1, Nicholas D Weatherley1, Guilhem J Collier1, Jim M Wild1, and Bilal A Tahir1,2
1POLARIS, Department of Infection, Immunity & Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom, 2Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom

Deep learning (DL)-based segmentation was conducted on a total of 431 3He and 129Xe 3D ventilation images using several training paradigms. Combined 3He and 129Xe training showed a significant improvement over all other DL methods. In the majority of DL models, no significant difference was observed between 3He and 129Xe testing data. Results suggest that 3He and 129Xe images share important features that allow combined 3He and 129Xe DL models to provide superior segmentations to singular gas models. In addition, it was shown that DL generates faster segmentations without the requirement of proton MRI compared to state-of-the-art model-based solutions.

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