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dc.contributor.authorWang, Yue
dc.contributor.authorXuan, Jianhua
dc.contributor.authorSrikanchana, Rujirutana
dc.contributor.authorChoyke, Peter L.
dc.identifier.citationYue Wang, Jianhua Xuan, Rujirutana Srikanchana, and Peter L. Choyke, “Modeling and Reconstruction of Mixed Functional and Molecular Patterns,” International Journal of Biomedical Imaging, vol. 2006, Article ID 29707, 9 pages, 2006. doi:10.1155/IJBI/2006/29707
dc.description.abstractFunctional medical imaging promises powerful tools for thevisualization and elucidation of important disease-causingbiological processes in living tissue. Recent research aims todissect the distribution or expression of multiple biomarkersassociated with disease progression or response, where the signalsoften represent a composite of more than one distinct sourceindependent of spatial resolution. Formulating the task as a blindsource separation or composite signal factorization problem, wereport here a statistically principled method for modeling andreconstruction of mixed functional or molecular patterns. Thecomputational algorithm is based on a latent variable model whoseparameters are estimated using clustered component analysis. Wedemonstrate the principle and performance of the approaches on thebreast cancer data sets acquired by dynamic contrast-enhancedmagnetic resonance imaging.
dc.rightsCreative Commons Attribution 4.0 International
dc.titleModeling and Reconstruction of Mixed Functional and Molecular Patternsen_US
dc.typeArticle - Refereed
dc.description.versionPeer Reviewed
dc.rights.holderCopyright © 2006 Yue Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International