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ABSTRACT: 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.htmContact
yuewang@vt.eduSupplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Chen L
PROVIDER: S-EPMC3167053 | biostudies-literature | 2011 Sep
REPOSITORIES: biostudies-literature
Chen Li L Chan Tsung-Han TH Choyke Peter L PL Hillman Elizabeth M C EM Chi Chong-Yung CY Bhujwalla Zaver M ZM Wang Ge G Wang Sean S SS Szabo Zsolt Z Wang Yue Y
Bioinformatics (Oxford, England) 20110723 18
<h4>Summary</h4>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 auto ...[more]