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Abstract #4024

Ultra-short Echo-time MRI Lung Segmentation using High-Dimensional Features and Continuous Max-Flow

Fumin Guo1, Khadija Sheikh1, Robert Peters2, Michael Carl2, Aaron Fenster1, and Grace Parraga1

1Robarts Research Institute, London, ON, Canada, 2General Electric Healthcare, Milwaukee, WI, United States

Ultra-short-echo-time MRI may be used to generate imaging biomarkers to phenotype pulmonary abnormalities and facilitate the development of novel treatments but requires clinically-acceptable lung segmentation. We proposed an adaptive kernel K-means approach combining MRI signal intensity and neighbourhood location information for optimized lung segmentation. The resultant high dimensional features were implemented using a K-nearest neighbour graph and relaxed to a point-wise upper-bound formulation regularized by image edge information, which was implemented iteratively using a continuous max-flow optimization approach. Experimental results for 10 asthmatics demonstrated highly accurate, reproducible and computationally efficient lung segmentation for our approach consistent with clinical workflows.

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