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Abstract #0916

Statistical combinations of T1, MTR, MTsat and Macromolecular Tissue Volume to improve myelin content estimation in the human spinal cord at 3T

Simon Lévy1, Ali Khatibi2,3,4,5, Gabriel Mangeat1, Jen-I Chen2,6, Kristina Martinu2, Pierre Rainville2,6, Nikola Stikov1,7, and Julien Cohen-Adad1,8

1NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada, 2Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada, 3Psychology Department, Bilkent University, Ankara-06800, Turkey, 4Interdisciplinary program in Neuroscience, Bilkent University, Ankara-06800, Turkey, 5National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara-06800, Turkey, 6Department of Stomatology, Faculty of Dentistry, Université de Montréal, Montreal, QC, Canada, 7Montreal Heart Institute, Montreal, QC, Canada, 8Functional Neuroimaging Unit, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montreal, QC, Canada

Several quantitative MRI metrics have been proposed to quantify myelin in the central nervous system but each of them includes confounding factors that impair their sensitivity and specificity. Because these factors are different across metrics, data driven approaches developed for blind source separation problems to extract the common component between recordings of the same sources seem appropriate. This study compares linear and nonlinear methods to combine myelin-sensitive metrics: T1, MTR, MTsat, MTV (1 – PD). The repeatability of the resulting combined metrics as well as their sensitivity to different microstructural features are tested. A higher sensitivity is achieved with linear combinations.

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