Dynamic susceptibility contrast (DSC) MRI is now increasingly used for measuring perfusion in many different applications. The quantification of DSC data requires the measurement of the arterial input function (AIF) and the deconvolution of the tissue concentration time curve. One of the most accepted deconvolution methods is the use of singular value decomposition (SVD). Simulations were performed to evaluate the effects on DSC quantification of the presence of delay and dispersion in the estimated AIF. Both delay and dispersion were found to introduce significant underestimation of cerebral blood flow (CBF) and overestimation of mean transit time (MTT). While the error introduced by the delay can be corrected by using the information of the arrival time of the bolus, the correction for the dispersion is less straightforward and requires a model for the vasculature.