SUMMARY: In vivo dynamic contrast-enhanced imaging tools provide non-invasive methods for analyzing various functional changes associated with disease initiation, progression and responses to therapy. The quantitative application of these tools has been hindered by its inability to accurately resolve and characterize targeted tissues due to spatially mixed tissue heterogeneity. Convex Analysis of Mixtures - Compartment Modeling (CAM-CM) signal deconvolution tool has been developed to automatically identify pure-volume pixels located at the corners of the clustered pixel time series scatter simplex and subsequently estimate tissue-specific pharmacokinetic parameters. CAM-CM can dissect complex tissues into regions with differential tracer kinetics at pixel-wise resolution and provide a systems biology tool for defining imaging signatures predictive of phenotypes. AVAILABILITY: The MATLAB source code can be downloaded at the authors' website www.cbil.ece.vt.edu/software.htm CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancer is a complex disease exhibiting a host of phenotypic diversities. Noninvasive multinuclear magnetic resonance imaging (MRI) and spectroscopic imaging (MRSI) provide an array of capabilities to characterize and understand several of the vascular, metabolic, and physiological characteristics unique to cancer. The availability of targeted contrast agents has widened the scope of MR techniques to include the detection of receptor and gene expression. In this paper, we have highlighted the application of several MR techniques in imaging and understanding cancer.