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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