Abstract #5094

Automated Seed Selection for Gradient-based Electrical Properties Tomography and Its in vivo Validation in the Brain

Yicun Wang¹, Pierre-Francois Van de Moortele², and Bin He^1,3

¹Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States, ²Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, ³Institute for Engineering in Medicine, University of Minnesota, Minneapolis, MN, United States

Electrical Properties Tomography (EPT) retrieves tissue electrical conductivity and permittivity at Larmor frequency which potentially provides diagnostic information and facilitates subject-specific local SAR estimation. Gradient-based EPT (gEPT) significantly alleviates boundary artifact encountered by conventional EPT methods, yet its implementation requires subjective assignment of integration seed points. In this study, we developed an automated seed selection strategy based on locally calculated conductivity values, and evaluated the effect of seed number for human brain imaging. This new strategy was validated in eight healthy subjects to produce robust and accurate results, paving the path for an unbiased and fully-automated process for EP quantification.

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