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.