Modeling and Reconstruction of Mixed Functional and Molecular Patterns.
- Resource Type
- Article
- Authors
- Yue Wang; Jianhua Xuan; Srikanchana, Rujirutana; Choyke, Peter L.
- Source
- International Journal of Biomedical Imaging. 2006, p1-9. 9p.
- Subject
- *MEDICAL imaging systems
*BIOMARKERS
*TISSUES
*ALGORITHMS
*LATENT variables
*BREAST cancer
*MAGNETIC resonance imaging of cancer
- Language
- ISSN
- 1687-4188
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. [ABSTRACT FROM AUTHOR]