MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.

ABSTRACT: Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream analyses on it. Such a challenge calls for data normalization and/or harmonization frameworks, in addition to comprehensive criteria to estimate the scanner-related variability and evaluate the harmonization frameworks. In this study, we propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a supervised multi-scanner harmonization method that is nat

SUBMITTER: Torbati ME 

PROVIDER: S-EPMC10529705 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature