An automatic procedure for normalization of cerebral blood volume maps in dynamic susceptibility contrast-based glioma imaging

Abstract

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.