LAUSR

In medical imaging, harmonization is pivotal for mitigating variability stemming from diverse imaging devices and protocols. Virtual imaging trials (VITs) can provide a way to simulate diverse imaging conditions in silico and thus provide a unique opportunity to assess the impact of such data variability on the performance of artificial intelligence (AI) models and quantitative analyses, and further to harmonize across these sources of variability. This variability underscores the need for systematic harmonization. Systematic harmonization helps to ensure data consistency for AI and quantitative analyses, ultimately enabling more generalizable model performance.