Separating composite signals in multi-probe dynamic biomedical imaging

Li Chen; Yue Wang; Chong Yung Chi; Zsolt Szabo; Peter L. Choyke

doi:10.1109/ACSSC.2007.4487153

Separating composite signals in multi-probe dynamic biomedical imaging

Li Chen, Yue Wang, Chong Yung Chi, Zsolt Szabo, Peter L. Choyke

School of Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Multi-probe dynamic biomedical imaging promises powerful tools for the visualization and elucidation of complex biological processes. Recent research aims to simultaneously dissect the spatial-temporal distributions of source signals that often represent a composite of multiple biomarkers independent of spatial resolution. We report a hybrid unmixing method for separating non-negative dependent imaging biomarker mixtures. The geodesic-principled algorithm exploits partial-volume modeling, non-negative clustered component analysis, and convex pyramid analysis, aided by a spatial-temporal coordinated information visualization aid. We demonstrate the principle of the approach on dynamic contrast-enhanced magnetic resonance imaging data and observed the expected vascular permeability and perfusion patterns due to tumor-induced angiogenesis and responses to therapy.

Original language	English (US)
Title of host publication	Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Pages	9-12
Number of pages	4
DOIs	https://doi.org/10.1109/ACSSC.2007.4487153
State	Published - 2007
Event	41st Asilomar Conference on Signals, Systems and Computers, ACSSC - Pacific Grove, CA, United States Duration: Nov 4 2007 → Nov 7 2007

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	41st Asilomar Conference on Signals, Systems and Computers, ACSSC
Country/Territory	United States
City	Pacific Grove, CA
Period	11/4/07 → 11/7/07

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2007.4487153

Cite this

Chen, L., Wang, Y., Chi, C. Y., Szabo, Z., & Choyke, P. L. (2007). Separating composite signals in multi-probe dynamic biomedical imaging. In Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC (pp. 9-12). Article 4487153 (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2007.4487153

Separating composite signals in multi-probe dynamic biomedical imaging. / Chen, Li; Wang, Yue; Chi, Chong Yung et al.
Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC. 2007. p. 9-12 4487153 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, L, Wang, Y, Chi, CY, Szabo, Z & Choyke, PL 2007, Separating composite signals in multi-probe dynamic biomedical imaging. in Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC., 4487153, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 9-12, 41st Asilomar Conference on Signals, Systems and Computers, ACSSC, Pacific Grove, CA, United States, 11/4/07. https://doi.org/10.1109/ACSSC.2007.4487153

@inproceedings{839cfa1b2166424e8d7c0d640b889f89,

title = "Separating composite signals in multi-probe dynamic biomedical imaging",

abstract = "Multi-probe dynamic biomedical imaging promises powerful tools for the visualization and elucidation of complex biological processes. Recent research aims to simultaneously dissect the spatial-temporal distributions of source signals that often represent a composite of multiple biomarkers independent of spatial resolution. We report a hybrid unmixing method for separating non-negative dependent imaging biomarker mixtures. The geodesic-principled algorithm exploits partial-volume modeling, non-negative clustered component analysis, and convex pyramid analysis, aided by a spatial-temporal coordinated information visualization aid. We demonstrate the principle of the approach on dynamic contrast-enhanced magnetic resonance imaging data and observed the expected vascular permeability and perfusion patterns due to tumor-induced angiogenesis and responses to therapy.",

author = "Li Chen and Yue Wang and Chi, {Chong Yung} and Zsolt Szabo and Choyke, {Peter L.}",

year = "2007",

doi = "10.1109/ACSSC.2007.4487153",

language = "English (US)",

isbn = "9781424421107",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

pages = "9--12",

booktitle = "Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC",

note = "41st Asilomar Conference on Signals, Systems and Computers, ACSSC ; Conference date: 04-11-2007 Through 07-11-2007",

}

TY - GEN

T1 - Separating composite signals in multi-probe dynamic biomedical imaging

AU - Chen, Li

AU - Wang, Yue

AU - Chi, Chong Yung

AU - Szabo, Zsolt

AU - Choyke, Peter L.

PY - 2007

Y1 - 2007

N2 - Multi-probe dynamic biomedical imaging promises powerful tools for the visualization and elucidation of complex biological processes. Recent research aims to simultaneously dissect the spatial-temporal distributions of source signals that often represent a composite of multiple biomarkers independent of spatial resolution. We report a hybrid unmixing method for separating non-negative dependent imaging biomarker mixtures. The geodesic-principled algorithm exploits partial-volume modeling, non-negative clustered component analysis, and convex pyramid analysis, aided by a spatial-temporal coordinated information visualization aid. We demonstrate the principle of the approach on dynamic contrast-enhanced magnetic resonance imaging data and observed the expected vascular permeability and perfusion patterns due to tumor-induced angiogenesis and responses to therapy.

AB - Multi-probe dynamic biomedical imaging promises powerful tools for the visualization and elucidation of complex biological processes. Recent research aims to simultaneously dissect the spatial-temporal distributions of source signals that often represent a composite of multiple biomarkers independent of spatial resolution. We report a hybrid unmixing method for separating non-negative dependent imaging biomarker mixtures. The geodesic-principled algorithm exploits partial-volume modeling, non-negative clustered component analysis, and convex pyramid analysis, aided by a spatial-temporal coordinated information visualization aid. We demonstrate the principle of the approach on dynamic contrast-enhanced magnetic resonance imaging data and observed the expected vascular permeability and perfusion patterns due to tumor-induced angiogenesis and responses to therapy.

UR - http://www.scopus.com/inward/record.url?scp=50249153037&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=50249153037&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2007.4487153

DO - 10.1109/ACSSC.2007.4487153

M3 - Conference contribution

AN - SCOPUS:50249153037

SN - 9781424421107

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 9

EP - 12

BT - Conference Record of the 41st Asilomar Conference on Signals, Systems and Computers, ACSSC

T2 - 41st Asilomar Conference on Signals, Systems and Computers, ACSSC

Y2 - 4 November 2007 through 7 November 2007

ER -

Separating composite signals in multi-probe dynamic biomedical imaging

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this