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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