CAM-CM: A signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues

Li Chen; Tsung Han Chan; Peter L. Choyke; Elizabeth M.C. Hillman; Chong Yung Chi; Zaver M. Bhujwalla; Ge Wang; Sean S. Wang; Zsolt Szabo; Yue Wang

doi:10.1093/bioinformatics/btr436

CAM-CM: A signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues

Li Chen, Tsung Han Chan, Peter L. Choyke, Elizabeth M.C. Hillman, Chong Yung Chi, Zaver M. Bhujwalla, Ge Wang, Sean S. Wang, Zsolt Szabo, Yue Wang

School of Medicine

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

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.

Original language	English (US)
Article number	btr436
Pages (from-to)	2607-2609
Number of pages	3
Journal	Bioinformatics
Volume	27
Issue number	18
DOIs	https://doi.org/10.1093/bioinformatics/btr436
State	Published - Sep 2011

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/btr436

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title = "CAM-CM: A signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues",

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.",

author = "Li Chen and Chan, {Tsung Han} and Choyke, {Peter L.} and Hillman, {Elizabeth M.C.} and Chi, {Chong Yung} and Bhujwalla, {Zaver M.} and Ge Wang and Wang, {Sean S.} and Zsolt Szabo and Yue Wang",

note = "Funding Information: Funding: National Institutes of Health, under Grants (EB000830, EB008627 and HHSN261200800001E) in part.",

year = "2011",

month = sep,

doi = "10.1093/bioinformatics/btr436",

language = "English (US)",

volume = "27",

pages = "2607--2609",

journal = "Bioinformatics",

issn = "1367-4803",

publisher = "Oxford University Press",

number = "18",

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T1 - CAM-CM

T2 - A signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues

AU - Chen, Li

AU - Chan, Tsung Han

AU - Choyke, Peter L.

AU - Hillman, Elizabeth M.C.

AU - Chi, Chong Yung

AU - Bhujwalla, Zaver M.

AU - Wang, Ge

AU - Wang, Sean S.

AU - Szabo, Zsolt

AU - Wang, Yue

N1 - Funding Information: Funding: National Institutes of Health, under Grants (EB000830, EB008627 and HHSN261200800001E) in part.

PY - 2011/9

Y1 - 2011/9

N2 - 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.

AB - 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.

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CAM-CM: A signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues

Abstract

ASJC Scopus subject areas

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