A diagnostic index based on pseudo-continuous arterial spin labeling and T1-mapping improves efficacy in discriminating Alzheimer’s disease from normal cognition

BackgroundPseudo-continuous arterial spin labeling (pCASL) is widely used to quantify cerebral blood flow (CBF) abnormalities in patients with Alzheimer's disease (AD). T1-mapping techniques assess microstructural characteristics in various pathologic changes, but their application in AD remains in the exploratory stage. We hypothesized that combining quantitative CBF and T1 values would generate diagnostic results with higher accuracy than using either method alone in discriminating AD patients from cognitively normal control (NC) subjects. Materials and methodsA total of 45 patients diagnosed with AD and 33 NC subjects were enrolled, and cognitive assessment was performed for each participant according to the Chinese version of the Mini-Mental State Examination (MMSE). T1-weighted magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) and pCASL sequence were scanned on a 3T MR scanner. A brain morphometric analysis was integrated into prototype sequence, providing tissue classification and morphometric segmentation results. Quantitative CBF and T1 values of each brain region were automatically generated inline after data acquisition. Independent samples t-test was used to compare regional CBF and T1 values controlled by false discovery rate correction (corrected p < 0.01). The model with combined CBF and T1 values was compared with the individual index by performing receiver operating characteristic curves analysis. The associations between the MMSE score and CBF and T1 values of the brain were investigated using partial correlations. ResultsCerebral blood flow of the right caudate nucleus (RCc) and left hippocampus (LHc) was significantly lower in the AD group compared with the NC group, while the T1 values of the right caudate nucleus (RCt) and left hippocampus (LHt) increased in the AD group. Prediction accuracies of 73.1, 77.2, 75.9, and 81.3% were achieved for each of the above parameters, respectively. In distinguishing patients from controls using the corresponding optimized cut-off values, most combinations of parameters were elevated (area under curve = 0.775-0.894). The highest area under curve value was 0.944, by combining RCc, LHc, RCt, and LHt. ConclusionIn this preliminary study, the combined model based on pCASL and T1-mapping improved the diagnostic performance of discriminating AD and NC groups. T1-mapping may become a competitive technique for quantitatively measuring pathologic changes in the brain.