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Abstract #2376

Fast multivariate relaxometry can differentiate neurodegenerative disease processes and phenotypes

Gabriel Mangeat1,2, Benjamin De Leener1, Virginija Danylaité Karrenbauer3,4, Marcel Warntjes5,6, Nikola Stikov1,7, Caterina Mainero2,8, Julien Cohen-Adad1,9, and Tobias Granberg2,8,10,11

1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montreal, QC, Canada, 2Athinoula A. Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States, 3Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden, 4Department of Neurology, Karolinska University Hospital, Stockholm, Sweden, 5Center for Medical Imaging Science and Visualization, CMIV, Linköping, Sweden, 6SyntheticMR, Linköping, Sweden, 7Montreal Health Institute, Montreal, QC, Canada, 8Harvard Medical School, Boston, MA, United States, 9Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada, 10Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden, 11Department of Radiology, Karolinska University Hospital, Stockholm, Sweden

Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. Here, we present a framework to extract independent physiological sources of signal from time-efficient multiple quantitative relaxometry (T1, T2 and PD maps) to characterize varying degrees and mechanisms of tissue disruption. The method can aid in the differentiation of HDLS and MS (p=0.007), as well as identify MS subtypes (p=0.0007), which would be helpful in ensuring a correct diagnosis and treatment of these disorders.

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