Multisite harmonization of diffusion tensor image analysis along the perivascular space using the COMBined Association Test

Abstract

Abstract Purpose This multisite study aimed to use the COMBined Association Test (COMBAT), a harmonization technique that uses regression of covariates with an empirical Bayesian framework, to harmonize diffusion tensor image analysis along the perivascular space (DTI-ALPS) variations caused by scanner, site, and protocol differences. Materials and methods This study included multisite diffusion magnetic resonance imaging (dMRI) data of 45 patients with Alzheimer’s disease (AD) and 82 cognitively normal (CN) participants from the AD neuroimaging initiative database. The dMRI data were obtained with two b values (0 and 1000 s/mm2) from 27 institutions and three different 3-Tesla MRI scanners (two vendors). The ALPS index was calculated from multisite dMRI data, and COMBAT was used to harmonize the factors causing site variations. Welch’s t test was used, Cohen’s d was calculated to compare the difference in the ALPS index between AD and CN before and after harmonization, and Pearson’s correlation coefficient was calculated to assess the relationships between the ALPS index and the cognitive score, [18F] fluorodeoxyglucose (FDG)-positron emission tomography (PET), and [18F] florbetapir (AV45)-PET standardized uptake value ratios (SUVRs). Results COMBAT harmonized scanner differences and increased Cohen’s d of the left and right ALPS indexes between AD and CN from 0.288 to 0.438 and 0.328 to 0.480, respectively. The ALPS indexes were significantly different between AD and CN after harmonization (P < 0.05) but not before it. Moreover, Pearson’s correlation coefficients between the ALPS index and cognitive score, FDG-PET, and AV45-PET SUVRs were higher after harmonization than before it. Conclusion This study demonstrates the application of COMBAT harmonization to eliminate between-scanner, site, and protocol variations in the ALPS index calculated from DTI-ALPS using dMRI and possibly facilitate the use of the ALPS index in multi-center studies.