Analysis of input functions from different arterial branches with gamma variate functions and cluster analysis for quantitative blood volume measurements

Regional cerebral blood volume (rCBV) provides valuable information about t he nature and progress of diseases of the central nervous system. While rel ative rCBV maps can be derived directly from dynamic susceptibility contras t data, the arterial input function (AIF) has to be measured for absolute r CBV quantification. For determination of the AIF pixels located completely within a feeding artery must be selected. However, by using a region-of-int erest (ROT) based selection some confounding effects can occur, especially if single shot echo planar imaging (EPI) with low spatial resolution is use d. In this study we analyzed the influence of partial volume effects and sp atial misregistration due to frequency shifts induced by paramagnetic contr ast agents. We analyzed AIFs from the internal carotid artery (ICA), the ve rtebral artery (VA) and the middle cerebral artery (MCA) using gamma variat e function based parameterization. The concentration time curves (CTC) of s everal pixels which were selected on the basis of strong signal drop appear ed distorted during the bolus passage. Moreover, the amplitudes of input fu nctions derived from the MCA were smaller by a factor of three as compared to those of the ICA and VA. Simulations revealed that these effects can be attributed to a spatial shift of the vessel along phase-encoding direction during the passage of the bolus. We therefore developed a procedure for a p ixel selection based on cluster analysis which classifies pixels according to the parameters of the fitted gamma variate functions. This approach acco unted for misregistration of the vessel and yielded very consistent results for a group of normal subjects. (C) 2001 Elsevier Science Inc. All rights reserved.