Arterial spin labeling (ASL) provides a noninvasive measurement of cerebral blood flow (CBF). Due to relatively low spatial resolution, the accuracy of CBF measurement is affected by the partial volume (PV) effect. In general ASL sequence, multiple scans of perfusion image pairs are acquired temporally to improve the signal to noise ratio. Several spatial PV correction methods have been proposed for the simple averaging of pair-difference images, while the perfusion information of gray matter and white matter existed in multiple image pairs was totally ignored. In this study, a statistical model of perfusion mixtures inside each voxel for the 4D ASL sequence is first proposed. To solve the model, a simplified method is proposed, in which the linear regression (LR) method is first used to obtain initial estimates of spatial correction, then an EM (expectation maximization) method is used to obtain accurate estimation using temporal information. The combination of LR and EM method (EM-LR) can effectively utilize the spatial-temporal information of ASL data for PV correction and provide a theoretical solution to estimate the perfusion mixtures. Both simulated and in vivo data were used to evaluate the performance of proposed method, which demonstrated its superiority on PV correction, edge preserving, and noise suppression.