Abstract #5081

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

Melanie Freed^1,2, 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.