Abstract

Abstract—We present an algorithm for blindly recovering con-stituent source spectra from magnetic resonance (MR) chemical shift imaging (CSI) of the human brain. The algorithm, which we call constrained nonnegative matrix factorization (cNMF), does not enforce independence or sparsity, instead only requiring the source and mixing matrices to be nonnegative. It is based on the nonnegative matrix factorization (NMF) algorithm, extending it to include a constraint on the positivity of the amplitudes of the recovered spectra. This constraint enables recovery of physically meaningful spectra even in the presence of noise that causes a sig-nificant number of the observation amplitudes to be negative. We demonstrate and characterize the algorithm’s performance using P volumetric brain data, comparing the results with two dif-ferent blind source separation methods: Bayesian spectral decom-position (BSD) and nonnegative sparse coding (NNSC). We then in-corporate the cNMF algorithm into a hierarchical decomposition framework, showing that it can be used to recover tissue-specific spectra given a processing hierarchy that proceeds coarse-to-fine. We demonstrate the hierarchical procedure on H brain data and conclude that the computational efficiency of the algorithm makes it well-suited for use in diagnostic work-up. Index Terms—Blind source separation (BSS), chemical shift imaging (CSI), hierarchical decomposition, magnetic resonance (MR), magnetic resonance spectroscopy (MRS), nonnegative matrix factorization (NMF).