Abstract #3011

RIMLA: Reproducibility-Informed Method for Longitudinal Assessment

Veronica Ravano^1,2,3, Michaela Andelova⁴, Gian Franco Piredda^1,5, Stefan Sommer^1,6, Samuele Caneschi¹, Lucia Roccaro¹, Jan Krasenky⁷, Matej Kudrna⁷, Tomas Uher⁴, Ricardo A. Corredor-Jerez^1,2,3, Jonathan A. Disselhorst^1,2,3, Bénédicte Maréchal^1,2,3, Tom Hilbert^1,2,3, Jean-Philippe Thiran³, Jonas Richiardi², Dana Horakova⁴, Manuela Vaneckova⁷, and Tobias Kober^1,2,3

¹Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Geneva and Zurich, Switzerland, ²Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ³LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, ⁴Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University and General University of Prague, Prague, Czech Republic, ⁵CIBM Centre for Biomedical Imaging, Geneva, Switzerland, ⁶Swiss Centre for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland, ⁷Department of Radiology, First Faculty of Medicine, Charles University and General University of Prague, Prague, Czech Republic

Synopsis

Keywords: Data Processing, Reproductive, longitudinal analyses; quantitative biomarkers

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.

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