Structural Correlation-based Outlier Rejection (SCORE) algorithm for arterial spin labeling time series.

PURPOSE: To propose and validate Structural Correlation-based Outlier REjection (SCORE), a novel algorithm for removal of artifacts arising from outlier control-label pairs in 2D arterial spin labeling (ASL) data.
MATERIALS AND METHODS: The proposed method was assessed with respect to other state-of-the-art ASL signal processing approaches using 2D pulsed ASL data obtained with a 3T Siemens scanner from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Longitudinal data from control participants acquired 3 months apart were used to assess within-subject coefficient of variation (wsCV) based on the assumption that the optimal signal processing strategy will minimize control subject retest variability in Cerebral Blood Flow (CBF). SCORE was further evaluated by determining its sensitivity for distinguishing patients with Alzheimer's disease (AD) from controls based on hypoperfusion in predefined regions of interest (ROIs) that are known to be sensitive to AD-related changes.
RESULTS: SCORE coupled with a preprocessing step to discard a few extreme outliers (combined algorithm referred to as SCORE+) reduced wsCV up to 21% in gray matter and 39% in smaller ROIs compared to the reference algorithms. It also provided an average increase in effect size for patient-control differences of 50% compared to other algorithms in a priori ROIs sensitive to AD-related changes. This increase was statistically significant (P < 0.05) for the majority of the ROIs and methods as evaluated by permutation tests.
CONCLUSION: CBF maps generated with SCORE or SCORE + provide improved retest reliability in control subjects while simultaneously increasing sensitivity to pathological CBF effects between controls and patients. J. Magn. Reson. Imaging 2016 Level of Evidence: 2 J. MAGN. RESON. IMAGING 2017;45:1786-1797.