Abstract #2376

Fast multivariate relaxometry can differentiate neurodegenerative disease processes and phenotypes

Gabriel Mangeat^1,2, Benjamin De Leener¹, Virginija Danylaité Karrenbauer^3,4, Marcel Warntjes^5,6, Nikola Stikov^1,7, Caterina Mainero^2,8, Julien Cohen-Adad^1,9, and Tobias Granberg^2,8,10,11

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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