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 Khatibi^2,3,4,5, Gabriel Mangeat¹, Jen-I Chen^2,6, Kristina Martinu², Pierre Rainville^2,6, Nikola Stikov^1,7, and Julien Cohen-Adad^1,8

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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