A new methodology based on tensor algebra that uses a higher order singular value decomposition
to perform three-dimensional voxel reconstruction from a series of temporal images
obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed.
Principal component analysis (PCA) is used to robustly extract the spatial and temporal
image features and simultaneously de-noise the datasets. Tumour segmentation on
enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is
compared with that achieved using the proposed tensorial framework. The proposed algorithm
explores the correlations between spatial and temporal features in the tumours. The
multi-channel reconstruction enables improved breast tumour identification through
enhanced de-noising and improved intensity consistency. The reconstructed tumours have
clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering
in tumour regions of interest. A more homogenous intensity distribution is also observed,
enabling improved image contrast between tumours and background, especially in places
where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis
of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The
proposed reconstruction metrics should also find future applications in the assessment of
other reconstruction algorithms