Shuyan Du

Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain

We present an algorithm for blindly recovering constituent 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 significant number of the observation amplitudes to be negative. We demonstrate and characterize the algorithms performance using /sup 31/P volumetric brain data, comparing the results with two different blind source separation methods: Bayesian spectral decomposition (BSD) and nonnegative sparse coding (NNSC). We then incorporate 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 /sup 1/H brain data and conclude that the computational efficiency of the algorithm makes it well-suited for use in diagnostic work-up.

Spectrum separation resolves partial-volume effect of MRSI as demonstrated on brain tumor scans.

Magnetic resonance spectroscopic imaging (MRSI) is currently used clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and to evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability because of partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity, and measurement noise. We address these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. This ‘spectrum separation’ method uses the non‐negative matrix factorization algorithm, which simultaneously decomposes the observed spectra of multiple voxels into abundance distributions and constituent spectra. The accuracy of the estimated abundances is validated on phantom data. The presented results on 20 clinical cases of brain tumor show reduced cross‐subject variability. This is reflected in improved discrimination between high‐grade and low‐grade gliomas, which demonstrates the physiological relevance of the extracted spectra. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool. Copyright

Recovery of Metabolomic Spectral Sources using Non-negative Matrix Factorization

1H magnetic resonance spectra (MRS) of biofluids contain rich biochemical information about the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, such data have been shown to provide information for disease diagnosis as well as the effects of potential therapeutics. In this paper we describe a novel approach, using non-negative matrix factorization (NMF), for rapidly identifying metabolically meaningful spectral patterns in1H MRS. We show that the intensities of these identified spectral patterns can be related to the onset of, and recovery from, toxicity in both a time-related and dose-related fashion. These patterns can be seen as a new type of biomarker for the biological effect under study. We demonstrate, using k-means clustering, that the recovered patterns can be used to characterize the metabolic status of the animal during the experiment.

Blind recovery of biochemical markers of brain cancer in MRSI

We present a multi-resolution hierarchical application of the constrained non-negative matrix factorization (cNMF) algorithm for blindly recovering constituent source spectra in magnetic resonance spectroscopic imaging (MRSI). cNMF is an extension of non-negative matrix factorization (NMF) that includes a positivity constraint on amplitudes of recovered spectra. We apply cNMF hierarchically, with spectral recovery and subspace reduction constraining which observations are used in the next level of processing. The decomposition model recovers physically meaningful spectra which are highly tissue-specific, for example spectra indicative of tumor proliferation, given a processing hierarchy that proceeds coarse-to-fine. We demonstrate the decomposition procedure on /sup 1/H long TE brain MRS data. The results show recovery of markers for normal brain tissue, low proliferative tissue and highly proliferative tissue. The coarse-to-fine hierarchy also makes the algorithm computationally efficient, thus it is potentially well-suited for use in diagnostic work-up.

Spectral separation resolves partial volume effect in MRSI: A validation study

Magnetic resonance spectroscopic imaging (MRSI) is utilized clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability due to partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity and measurement noise. This study investigates spectral separation as a novel quantification tool, addressing these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. Present results on 20 clinical cases of brain tumors show reduced cross-subject variability. This reduced variability leads to improved discrimination between high and low-grade gliomas, confirming the physiological relevance of the extracted spectra. Further validation on phantom data demonstrates the accuracy of the estimated abundances. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool.

Automated Analysis of 1 H Magnetic Resonance Metabolic Imaging Data as an Aid to Clinical Decision-Making in the Evaluation of Intracranial Lesions

Proton magnetic resonance spectroscopic imaging ( 1H MRSI) is a noninvasive metabolic imaging technique that has emerged as a potentially powerful tool for complementing structural magnetic resonance imaging (MRI) in the clinical evaluation of neurological disorders and diagnostic decision making. However, the relative complexity of methods that are currently available for analyzing the derived multi-dimensional metabolic imaging data has slowed incorporation of the technique into routine clinical practice. This paper discusses this impediment to widespread clinical use of 1H MRSI and then describes an automated data analysis approach that promises to facilitate use of the technique in the evaluation of intracranial lesions, with the potential to enhance the specificity of MRI and improve clinical decision-making.

Multiresolution hierarchical blind recovery of biochemical markers of brain cancer in MRSI

We present a multi-resolution hierarchical application of the constrained non-negative matrix factorization (cNMF) algorithm for blindly recovering constituent source spectra in magnetic resonance spectroscopic imaging (MRSI). cNMF is an extension of non-negative matrix factorization (NMF) that includes a positivity constraint on amplitudes of recovered spectra. We apply cNMF hierarchically, with spectral recovery and subspace reduction constraining which observations are used in the next level of processing. The decomposition model recovers physically meaningful spectra which are highly tissue-specific, for example spectra indicative of tumor proliferation, given a processing hierarchy that proceeds coarse-to-fine. We demonstrate the decomposition procedure on /sup 1/H long TE brain MRS data. The results show recovery of markers for normal brain tissue, low proliferative tissue and highly proliferative tissue. The coarse-to-fine hierarchy also makes the algorithm computationally efficient, thus it is potentially well-suited for use in diagnostic work-up.