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Abstract #5081

A Maximum Likelihood Method for Partial Volume Segmentation of Magnitude Breast MR Data

Melanie Freed1,2, Christian Graff1, Maria I. Altbach3, Jacco A. de Zwart4, Jeff H. Duyn4, Aldo Badano1

1CDRH/OSEL/DIAM, FDA, Silver Spring, MD, United States; 2Department of Bioengineering, University of Maryland, College Park, MD, United States; 3Department of Radiology, University of Arizona, Tucson, AZ, United States; 4NINDS/LFMI/Advanced MRI Section, National Institutes of Health, Bethesda, MD, United States

We apply maximum likelihood estimation techniques to magnitude MR images as a method for partial volume segmentation. The method is validated on noisy inversion recovery and saturation recovery images of a simulated MR breast phantom created from human CT data and then applied to inversion recovery images of a physical breast phantom. The segmentation algorithm is able to successfully separate tissue types in both simulated and phantom MR images.