Three-dimensional MRSI data of the prostate was analyzed with a Multivariate Curve Resolution-Alternating (MCR) approach for rapid automated localization and classification of cancer and healthy tissue. This data-driven method was used to extract common spectroscopic components without a need of prior knowledge, and compared to fitting a linear combination of prior knowledge models (LCModel). The MCR method identified components with known prostate metabolites and residual lipid and water signals Altogether, our approach can be considered as a step towards the development of an automated tool for classification of prostate MRSI spectra, avoiding subjective human intervention.
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