Dynamic susceptibility contrast (DSC)-MRI is commonly used to measure cerebral perfusion in acute ischemic stroke. Quantification of perfusion parameters involves deconvolution of the tissue concentration-time curves with an arterial input function (AIF), typically with the use of singular value decomposition (SVD). To mitigate the effects of noise on the estimated cerebral blood flow (CBF), a regularization parameter or threshold is used. Often a single global threshold is applied to every voxel, and its value has a dramatic effect on the CBF values obtained. When a single global threshold was applied to simulated concentration-time curves produced using exponential, triangular, and boxcar residue functions, significant systematic errors were found in the measured perfusion parameters. We estimate the errors obtained for different sampling intervals and signal-to-noise ratios (SNRs), and discuss the source of the systematic error. We present a method that partially corrects for the systematic error in the presence of an exponential residue function by applying a linear fit, which removes underestimates of long mean transit time (MTT) and overestimates of short MTT. For example, the correction reduced the error at a temporal resolution of 2.5 s and an SNR of 30 from 29.1% to 11.7%. However, the error is largest in the presence of noise and at MTTs that are likely to be encountered in areas of hypoperfusion; furthermore, even though it is reduced, it cannot be corrected for exactly.