CAM-CM: a signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues
Author(s) -
Li Chen,
TsungHan Chan,
Peter L. Choyke,
Elizabeth M. C. Hillman,
ChongYung Chi,
Zaver M. Bhujwalla,
Ge Wang,
Sean S. Wang,
Zsolt Szabó,
Yue Wang
Publication year - 2011
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btr436
Subject(s) - deconvolution , contrast (vision) , signal (programming language) , dynamic contrast , computer science , in vivo , dynamic imaging , artificial intelligence , biomedical engineering , computer vision , algorithm , image processing , biology , radiology , medicine , image (mathematics) , magnetic resonance imaging , digital image processing , microbiology and biotechnology , programming language
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.
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