Computed simultaneous imaging of multiple biomarkers

Yue Wang; Jianhua Xuan; Rujirutana Srikanchana; Junying Zhang; Zsolt Szabo; Zaver Bhujwalla; Peter Choyke; King Li

doi:10.1109/NNSP.2003.1318026

Computed simultaneous imaging of multiple biomarkers

Yue Wang, Jianhua Xuan, Rujirutana Srikanchana, Junying Zhang, Zsolt Szabo, Zaver Bhujwalla, Peter Choyke, King Li

School of Medicine

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

2 Scopus citations

Abstract

Functional-molecular imaging techniques promise powerful tools for the visualization and elucidation of important disease-causing physiologic-molecular processes in living tissue. Most applications aim to find temporal-spatial patterns assocaited with different disease stages. When multiple agents are used, imagery signals often represent a composite of more than one distinct source due to functional-molecular biomarker heterogeneity, independent of spatial resolution. We therefore introduce a hybrid decomposition algorithm which allows for a computed simultaneous imaging of multiple biomarkers. The method is based on a combination of time-activity curve clustering, pixel subset selection, and independent component analysis. We demonstrate the principle of the approach on an image data set, and we then apply the method to the tumor vascular characterization using dynamic contrast-enhanced magnetic resonance imaging and brain neuro-transporter imaging using dynamic positron emission tomography.

Original language	English (US)
Title of host publication	2003 IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP 2003
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	269-278
Number of pages	10
ISBN (Electronic)	0780381777
DOIs	https://doi.org/10.1109/NNSP.2003.1318026
State	Published - 2003
Event	13th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2003 - Toulouse, France Duration: Sep 17 2003 → Sep 19 2003

Publication series

Name	Neural Networks for Signal Processing - Proceedings of the IEEE Workshop
Volume	2003-January

Other

Other	13th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2003
Country/Territory	France
City	Toulouse
Period	9/17/03 → 9/19/03

ASJC Scopus subject areas

Electrical and Electronic Engineering
Artificial Intelligence
Software
Computer Networks and Communications
Signal Processing

Access to Document

10.1109/NNSP.2003.1318026

Cite this

Wang, Y., Xuan, J., Srikanchana, R., Zhang, J., Szabo, Z., Bhujwalla, Z., Choyke, P., & Li, K. (2003). Computed simultaneous imaging of multiple biomarkers. In 2003 IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP 2003 (pp. 269-278). Article 1318026 (Neural Networks for Signal Processing - Proceedings of the IEEE Workshop; Vol. 2003-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NNSP.2003.1318026

Computed simultaneous imaging of multiple biomarkers. / Wang, Yue; Xuan, Jianhua; Srikanchana, Rujirutana et al.
2003 IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP 2003. Institute of Electrical and Electronics Engineers Inc., 2003. p. 269-278 1318026 (Neural Networks for Signal Processing - Proceedings of the IEEE Workshop; Vol. 2003-January).

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

Wang, Y, Xuan, J, Srikanchana, R, Zhang, J, Szabo, Z , Bhujwalla, Z, Choyke, P & Li, K 2003, Computed simultaneous imaging of multiple biomarkers. in 2003 IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP 2003., 1318026, Neural Networks for Signal Processing - Proceedings of the IEEE Workshop, vol. 2003-January, Institute of Electrical and Electronics Engineers Inc., pp. 269-278, 13th IEEE Workshop on Neural Networks for Signal Processing, NNSP 2003, Toulouse, France, 9/17/03. https://doi.org/10.1109/NNSP.2003.1318026

Wang Y, Xuan J, Srikanchana R, Zhang J, Szabo Z , Bhujwalla Z et al. Computed simultaneous imaging of multiple biomarkers. In 2003 IEEE 13th Workshop on Neural Networks for Signal Processing, NNSP 2003. Institute of Electrical and Electronics Engineers Inc. 2003. p. 269-278. 1318026. (Neural Networks for Signal Processing - Proceedings of the IEEE Workshop). doi: 10.1109/NNSP.2003.1318026

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abstract = "Functional-molecular imaging techniques promise powerful tools for the visualization and elucidation of important disease-causing physiologic-molecular processes in living tissue. Most applications aim to find temporal-spatial patterns assocaited with different disease stages. When multiple agents are used, imagery signals often represent a composite of more than one distinct source due to functional-molecular biomarker heterogeneity, independent of spatial resolution. We therefore introduce a hybrid decomposition algorithm which allows for a computed simultaneous imaging of multiple biomarkers. The method is based on a combination of time-activity curve clustering, pixel subset selection, and independent component analysis. We demonstrate the principle of the approach on an image data set, and we then apply the method to the tumor vascular characterization using dynamic contrast-enhanced magnetic resonance imaging and brain neuro-transporter imaging using dynamic positron emission tomography.",

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AU - Choyke, Peter

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AB - Functional-molecular imaging techniques promise powerful tools for the visualization and elucidation of important disease-causing physiologic-molecular processes in living tissue. Most applications aim to find temporal-spatial patterns assocaited with different disease stages. When multiple agents are used, imagery signals often represent a composite of more than one distinct source due to functional-molecular biomarker heterogeneity, independent of spatial resolution. We therefore introduce a hybrid decomposition algorithm which allows for a computed simultaneous imaging of multiple biomarkers. The method is based on a combination of time-activity curve clustering, pixel subset selection, and independent component analysis. We demonstrate the principle of the approach on an image data set, and we then apply the method to the tumor vascular characterization using dynamic contrast-enhanced magnetic resonance imaging and brain neuro-transporter imaging using dynamic positron emission tomography.

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Computed simultaneous imaging of multiple biomarkers

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