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Abstract #2471

Data-driven separation of MRI signal components for tissue characterization

Sofie Rahbek1, Kristoffer H. Madsen2,3, Henrik Lundell2, Faisal Mahmood4,5, and Lars G. Hanson1,2
1Department of Health Technology, Technical University of Denmark, Kgs. Lyngby, Denmark, 2Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark, 3Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark, 4Laboratory of Radiation Physics, Odense University Hospital, Odense, Denmark, 5Department of Clinical Research, University of Southern Denmark, Odense, Denmark

We propose a novel data-driven method for extraction of tissue-related signal components from high-dimensional MRI data. In this method, the standard non-negative matrix factorization (NMF) has been extended with signal monotonicity constraints suitable for several MR signal types, and is termed the monotonous slope NMF (msNMF). Its applications are here demonstrated using both diffusion-weighted and relaxometry data. The msNMF successfully distinguish areas with different cell densities and levels of white matter intra-myelinic edema, respectively, and is potentially useful for diagnosis and therapy evaluation.

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