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Published on: June 16, 2014
CAM-CM: a signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues
Li Chen1, Tsung-Han Chan, Peter L Choyke
1Bradley Department of Electrical and Computer Engineering, Virginia Tech, Arlington, VA 22203, USA.
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:
yuewang@vt.edu
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
A new Convex Analysis of Mixtures - Compartment Modeling (CAM-CM) tool enhances in vivo dynamic contrast-enhanced imaging. This method accurately analyzes tissue heterogeneity for improved disease diagnosis and treatment monitoring.
Area of Science:
- Biomedical Imaging
- Pharmacokinetics
- Systems Biology
Background:
- In vivo dynamic contrast-enhanced imaging offers non-invasive analysis of functional changes in disease and therapy response.
- Quantitative imaging is limited by the inability to resolve tissue heterogeneity in mixed tissues.
- Pixel time series scatter simplex clustering reveals tissue heterogeneity.
Purpose of the Study:
- To develop a novel signal deconvolution tool, Convex Analysis of Mixtures - Compartment Modeling (CAM-CM).
- To enable accurate characterization of targeted tissues and estimation of tissue-specific pharmacokinetic parameters.
- To provide a systems biology tool for defining imaging signatures predictive of phenotypes.
Main Methods:
- CAM-CM automatically identifies pure-volume pixels within clustered pixel time series scatter simplex.
- Signal deconvolution is applied to estimate tissue-specific pharmacokinetic parameters.
- Pixel-wise resolution is achieved to dissect complex tissues into regions with differential tracer kinetics.
Main Results:
- CAM-CM successfully identifies pure-volume pixels, overcoming limitations of tissue heterogeneity.
- Accurate estimation of tissue-specific pharmacokinetic parameters is achieved.
- The tool enables dissection of complex tissues into regions with distinct tracer kinetics.
Conclusions:
- CAM-CM enhances quantitative analysis in dynamic contrast-enhanced imaging by addressing tissue heterogeneity.
- This method provides a systems biology approach for identifying imaging biomarkers for disease phenotypes.
- CAM-CM offers a powerful tool for disease initiation, progression, and therapy response analysis.
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