Modeling and Reconstruction of Mixed Functional and Molecular Patterns
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 th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2006-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/IJBI/2006/29707 |
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| Summary: | 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. |
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| ISSN: | 1687-4188 1687-4196 |