Harmonizing Multicenter Datasets: Enhancing Consistency and Longitudinal Alignment using NLP and Realignment Algorithms

Motivation: Cohesive multicenter imaging datasets are critical for research, yet variability across institutions poses a significant challenge, especially when aggregating retrospective data for longitudinal disease monitoring. Goal(s): Here, we present a method for harmonizing multicenter data that produces consistent series descriptions and enhances brain alignment between longitudinal time points. Approach: We employed an NLP pipeline to standardize series descriptions and an automated algorithm to realign images. We applied these tools to ADNI imaging collected across multiple sites, scanners, and time points. Results: The pipeline consolidated 101 unique series descriptions into 17 standardized descriptions. The alignment algorithm reduced orientation error and improved longitudinal image consistency. Impact: Our methodology can impact clinical workflows by streamlining multicenter data analysis and enhancing longitudinal disease monitoring. These techniques improve image consistency between time points, which can facilitate disease monitoring and allow radiologists to assess changes in chronic disorders.