Modeling and Reconstruction of Mixed Functional and Molecular Patterns

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Functional 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.




Yue 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