Tagged: iterative data selection

Automated Tissue Segmentation and Blind Recovery of 1H MRSI Spectral Patterns of Normal and Diseased Human Brain

Constrained non-negative matrix factorization (cNMF) with iterative data selection is described and demonstrated as a data analysis method for fast and automatic recovery of biochemically meaningful and diagnostically specific spectral patterns of the human brain from 1H MRS imaging (1H MRSI) data. To achieve this goal, cNMF decomposes in vivo multidimensional 1H MRSI data into two non-negative matrices representing (a) the underlying tissue-specific spectral patterns and (b) the spatial distribution of the corresponding metabolite concentrations. Central to the proposed approach is automatic iterative data selection which uses prior knowledge about the spatial distribution of the spectra to remove voxels that are due to artifacts and undesired metabolites/tissues such as the strong lipid and water components. The automatic recovery of diagnostic spectral patterns is demonstrated for long-TE1H MRSI data on normal human brain, multiple sclerosis, and serial brain tumor. The results show the ability of cNMF with iterative data selection to automatically and simultaneously recover tissue-specific spectral patterns and achieve segmentation of normal and diseased human brain tissue, concomitant with simplification of information content. These features of cNMF, which permit rapid recovery, reduction and interpretation of the complex diagnostic information content of large multi-dimensional spectroscopic imaging data sets, have the potential to enhance the clinical utility of in vivo1H MRSI.