Background: Arterial spin labeling (ASL) provides a noninvasive way to measure cerebral\nblood flow (CBF). The CBF estimation from ASL is heavily contaminated by noise\nand the partial volume (PV) effect. The multiple measurements of perfusion signals in\nthe ASL sequence are generally acquired and were averaged to suppress the noise.\nTo correct the PV effect, several methods were proposed, but they were all performed\ndirectly on the averaged image, thereby ignoring the inherent perfusion information\nof mixed tissues that are embedded in multiple measurements. The aim of the present\nstudy is to correct the PV effect of ASL sequence using the inherent perfusion information\nin the multiple measurements.\nMethods: In this study, we first proposed a statistical perfusion model of mixed tissues\nbased on the distribution of multiple measurements. Based on the tissue mixture that\nwas obtained from the high-resolution structural image, a structure-based expectation\nmaximization (sEM) scheme was developed to estimate the perfusion contributions of\ndifferent tissues in a mixed voxel from its multiple measurements. Finally, the performance\nof the proposed method was evaluated using both computer simulations and\nin vivo data.\nResults: Compared to the widely used linear regression (LR) method, the proposed\nsEM-based method performs better on edge preservation, noise suppression, and\nlesion detection, and demonstrates a potential to estimate the CBF within a shorter\nscanning time. For in vivo data, the corrected CBF values of gray matter (GM) were\nindependent of the GM probability, thereby indicating the effectiveness of the sEMbased\nmethod for the PV correction of the ASL sequence.\nConclusions: This study validates the proposed sEM scheme for the statistical perfusion\nmodel of mixed tissues and demonstrates the effectiveness of using inherent\nperfusion information in the multiple measurements for PV correction of the ASL\nsequence.
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