RIMLA: Reproducibility-Informed Method for Longitudinal Assessment

Abstract

Motivation: Estimating longitudinal changes in imaging biomarkers is challenging due to the multiple sources of variation during acquisition that can influence the analysis of MRI data. Goal(s): To provide a robust estimate of longitudinal changes based on the comparison of cross-sectional imaging biomarkers from different time points. Approach: We introduce RIMLA, a Reproducibility-Informed Method for Longitudinal Assessment that quantifies longitudinal imaging biomarker changes while accounting for the robustness of the underlying image processing algorithm. Results: As a first application, we show that RIMLA allows to identify multiple sclerosis lesion subtypes characterized by statistically significant enlargement or shrinkage over time. Impact: The here introduced Reproducibility-Informed Method for Longitudinal Assessment (RIMLA) allows to robustly detect small longitudinal changes in quantitative biomarkers. This increase in sensitivity can lead to better informed clinical decisions, for example during treatment monitoring or disease progression follow-ups.