Abstract #1528

Automated Segmentation of Liver Parenchyma and Blood Vessel with in-vivo Radial Gradient and Spin-Echo (GRASE) Datasets for Characterization of Diffuse Liver Disease

Abhishek Pandey¹,², Ali Bilgin¹,³, Sindhu Cumar², Bobby T. Kalb², Diego R. Martin², Maria I. Altbach²

¹Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States; ²Medical Imaging, University of Arizona, Tucson, AZ, United States; ³Biomedical Engineering, University of Arizona, Tucson, AZ, United States

The analysis of imaging parameters in the liver has become of increased importance for the evaluation of pathologies such as fibrosis, inflammation and iron deposition. Although parametric imaging techniques have been developed, the analysis of parameters maps is mainly restricted to an ROI within the liver. Whole liver analysis should yield a better representation of the disease and if the analysis is automated it can be used routinely in the clinic. In this work, we present a combined liver and vessel segmentation technique that is used to evaluate full liver T2 and Fat Fraction maps in an automated fashion.