We present a new concept based on matrix analysis for analyzing hybrid multi-dimensional T2-weighted and diffusion-weighted MRI of the prostate. This study evaluates whether matrix analysis is useful in diagnosis of prostate cancer. The hybrid data were linearized first by taking natural logarithms. Then the hybrid symmetric matrix was formed by multiplying by its own transpose matrix for each pixel. The eigenvalues and eigenvectors are calculated for this symmetric matrix to generate color maps. The preliminary results suggest that the combined color eigenvalue map provides new information that could help to identify and stage prostate cancer.
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