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PLoS One. 2017 Mar 10;12(3):e0172111. doi: 10.1371/journal.pone.0172111. eCollection 2017.

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

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Centre for Applied Informatics School of Engineering and Science, Victoria University, Melbourne, Australia.
School of Systems Engineering and Department of Bioengineering, University of Reading, Reading RG6 6AY, United Kingdom.
Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, United States of America.
Department of Radiology, EDa Hospital and I-Shou University, Kaohsiung, Taiwan.
School of Computer Science, Fudan University, China.
Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China.


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.

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