Tagged: matrix decomposition

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.

Multi-resolution 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.

Removal of BCG artifacts using a non-Kirchhoffian overcomplete representation

We present a nonlinear unmixing approach for extracting the ballistocardiogram (BCG) from EEG recorded in an MR scanner during simultaneous acquisition of functional MRI (fMRI). First, an overcomplete basis is identified in the EEG based on a custom multipath EEG electrode cap. Next, the overcomplete basis is used to infer non-Kirchhoffian latent variables that are not consistent with a conservative electric field. Neural activity is strictly Kirchhoffian while the BCG artifact is not, and the representation can hence be used to remove the artifacts from the data in a way that does not attenuate the neural signals needed for optimal single-trial classification performance. We compare our method to more standard methods for BCG removal, namely independent component analysis and optimal basis sets, by looking at single-trial classification performance for an auditory oddball experiment. We show that our overcomplete representation method for removing BCG artifacts results in better single-trial classification performance compared to the conventional approaches, indicating that the derived neural activity in this representation retains the complex information in the trial-to-trial variability.