RIMLA: Reproducibility-Informed Method for Longitudinal Assessment

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.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2024-11-26
DOI
https://doi.org/10.58530/2024/3011
Primary Topic
Advanced Data Processing Techniques
Type
article
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article

RIMLA: Reproducibility-Informed Method for Longitudinal Assessment

Veronica Ravano, Tom Hilbert, Dana Horáková, Tomáš Uher et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced Data Processing Techniques
article

RIMLA: Reproducibility-Informed Method for Longitudinal Assessment

Veronica Ravano, Tom Hilbert, Dana Horáková, Tomáš Uher, Ricardo Corredor‐Jerez, Jonathan A. Disselhorst, Jonas Richiardi, Michaela Andělová, Bénédicte Maréchal, Gian Franco Piredda, Samuele Caneschi, Stefan Sommer, Jean‐Philippe Thiran, Tobias Kober, Matej Kudrna, Manuela Vaněčková, Lucia Roccaro, Jan Krasenky
article en

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.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Good health and well-being
Openalex Percentile: Top 75%
Advanced Data Processing Techniques
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