ASL suffers from relatively low signal-to-noise, so data cleaning strategies are required to optimise its utility. A previous method for outlier rejection of 2D-PASL data required time-consuming spatial normalization to standard space, degrading the original ASL data, and was limited to single inversion-time (TI) 2D-PASL. We therefore developed two native-space processing workflows, termed Native Space Outlier Rejection (NaSOR) and Native Space Perfusion-weighted Outlier Rejection (NaSPOR). The two native-space workflows performed comparably to an implementation of the previous standard-space method, in terms of both percentage of outliers rejected and coefficients of variation (CV) for test-retest CBF values, suggesting clinical utility.
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