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Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs

Yin, X. -X., Hadjiloucas, S., Chen, J. -H., Zhang, Y., Wu, J. -L. and Su, M. -Y. (2017) Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs. PLoS ONE, 12 (3). e0172111. ISSN 1932-6203 (e0172111.)

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To link to this item DOI: 10.1371/journal.pone.0172111


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.

Item Type:Article
Divisions:Interdisciplinary Research Centres (IDRCs) > Centre for Integrative Neuroscience and Neurodynamics (CINN)
Interdisciplinary centres and themes > Computational Sciences Centre
Life Sciences > School of Biological Sciences > Biomedical Sciences
Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:69725
Publisher:Public Library of Science


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