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

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 (...

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Autores principales: Yin, X. -X., Hadjiloucas, S., Chen, J. -H., Zhang, Y., Wu, J. -L., Su, M. -Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345763/
https://www.ncbi.nlm.nih.gov/pubmed/28282379
http://dx.doi.org/10.1371/journal.pone.0172111
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author Yin, X. -X.
Hadjiloucas, S.
Chen, J. -H.
Zhang, Y.
Wu, J. -L.
Su, M. -Y.
author_facet Yin, X. -X.
Hadjiloucas, S.
Chen, J. -H.
Zhang, Y.
Wu, J. -L.
Su, M. -Y.
author_sort Yin, X. -X.
collection PubMed
description 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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spelling pubmed-53457632017-03-30 Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs Yin, X. -X. Hadjiloucas, S. Chen, J. -H. Zhang, Y. Wu, J. -L. Su, M. -Y. PLoS One Research Article 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. Public Library of Science 2017-03-10 /pmc/articles/PMC5345763/ /pubmed/28282379 http://dx.doi.org/10.1371/journal.pone.0172111 Text en © 2017 Yin et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yin, X. -X.
Hadjiloucas, S.
Chen, J. -H.
Zhang, Y.
Wu, J. -L.
Su, M. -Y.
Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs
title Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs
title_full Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs
title_fullStr Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs
title_full_unstemmed Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs
title_short Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs
title_sort tensor based multichannel reconstruction for breast tumours identification from dce-mris
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345763/
https://www.ncbi.nlm.nih.gov/pubmed/28282379
http://dx.doi.org/10.1371/journal.pone.0172111
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