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Abstract #1101

Model Independent Method on Modified DCE-MRI Perfusion Data for Exploring Area and Grade of Gliomas

Bob L Hou 1 , Alice B Lai 2 , Guodong Guo 2 , and Jeffrey S Carpenter 1

1 Radiology, WVU, Morgantown, WV, United States, 2 Computer and EE, WVU, Morgantown, WV, United States

A common approaching to find brain tumor area and grade it from DCE-MRI perfusion data is to get the maps of volume transfer constant (Ktrans) and fractional extracellular-extravascular space volume (Ve) from pharmacokinetic models. However there are questions on the models, and by using the models is very difficult to distinguish the Grade III with the Grade IV gliomas. In this study, we sought to apply a model independent method, i.e., Probabilistic Independent Component Analysis (PICA), on modified DCE data for finding the tumor areas and distinguishing their grades.

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