Modeling and Reconstruction of Mixed Functional and Molecular Patterns
<p>Functional medical imaging promises powerful tools for the visualization and elucidation of important disease-causing biological processes in living tissue. Recent research aims to dissect the distribution or expression of multiple biomarkers associated with disease progression or response,...
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| Format: | Article |
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| Language: | English |
| Published: |
Wiley
2006-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://www.hindawi.com/GetArticle.aspx?doi=10.1155/IJBI/2006/29707 |
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| Summary: | <p>Functional medical imaging promises powerful tools for the visualization and elucidation of important disease-causing biological processes in living tissue. Recent research aims to dissect the distribution or expression of multiple biomarkers associated with disease progression or response, where the signals often represent a composite of more than one distinct source independent of spatial resolution. Formulating the task as a blind source separation or composite signal factorization problem, we report here a statistically principled method for modeling and reconstruction of mixed functional or molecular patterns. The computational algorithm is based on a latent variable model whose parameters are estimated using clustered component analysis. We demonstrate the principle and performance of the approaches on the breast cancer data sets acquired by dynamic contrast-enhanced magnetic resonance imaging.</p> |
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| ISSN: | 1687-4188 |