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To characterize gliomas from dynamic susceptibility contrast (DSC)-based cerebral blood volume (CBV) maps, a CBV value from a normal-appearing region of interest is typically identified manually and used to normalize the CBV maps. This method is user-dependent and time-consuming. We propose an alternative approach based on automatic identification of normal-appearing first-pass curves from brain tissue. Our results in 101 patients suggest similar or better diagnostic accuracy values than the manual approach.

An Automatic Procedure for Normalization of Cerebral Blood Volume Maps in Dynamic Susceptibility Contrast−Based Glioma Imaging