Abstract #2964

Cross-sectional multiparametric subtyping of multiple sclerosis lesions informs on their longitudinal evolution

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: Multiple Sclerosis, Relaxometry, White Matter; quantitative imaging

Motivation: In multiple sclerosis, slowly expanding lesions have been suggested as a hallmark of a steadily worsening disease course. However, identifying these lesions is challenging, as their growth rates are at the detection limit of today's processing algorithms or MRI data must be available over a long period of time.

Goal(s): To identify and characterise slowly expanding lesions in cross-sectional data.

Approach: We compared changes in quantitative T1, T2 and T2/T1-ratio inside lesions and in perilesional tissue for enlarging/stable/shrinking/new lesion phenotypes.

Results: Z-scores of multiparametric quantitative maps carry discriminative information to classify lesion evolution from single time point data.

Impact: Our findings suggest that quantitative multiparametric analyses allow a better in vivo characterisation of microstructural tissue pathology in multiple sclerosis; this furthers the understanding of different lesion evolutions and might enable to already distinguish them from cross-sectional data.

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