International Journal of Biomedical Imaging 
Volume 2006 (2006), Article ID 29707, 9 pages
doi:10.1155/IJBI/2006/29707

Modeling and Reconstruction of Mixed Functional and Molecular Patterns

Yue Wang,1 Jianhua Xuan,2 Rujirutana Srikanchana,3 and Peter L. Choyke4

 1Department of Electrical and Computer Engineering, Computational Bioinformatics and Bioimaging Laboratory, Virginia Polytechnic Institute and State University, 4300 Wilson Boulevard, Suite 750, Arlington 22203, VA, USA
2Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington 20064, DC, USA
3Riverain Medical, Riverain Research Group, Rockville 20850, MD, USA
4Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda 20892, MD, USA
Received 10 August 2005; Accepted 23 October 2005

Recommended by Ming Jiang

Abstract

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.