Dynamic physiological imaging and quantification of tumor microcirculation
In this project, we have developed parametric and non-parametric methods for analysis of Dynamic Contrast-Enhanced (DCE) imaging data, with the aim of studying tumor microcirculation. These include a generalized mamillary distributed- parameter model for capillary-tissue exchange, a regression appro...
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sg-ntu-dr.10356-28532023-03-04T03:24:42Z Dynamic physiological imaging and quantification of tumor microcirculation Koh, Tong San. School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics In this project, we have developed parametric and non-parametric methods for analysis of Dynamic Contrast-Enhanced (DCE) imaging data, with the aim of studying tumor microcirculation. These include a generalized mamillary distributed- parameter model for capillary-tissue exchange, a regression approach to regularization using both conventional and generalized Singular Value Decomposition (SVD), and the use of piecewise continuous regression models for the automatic estimation of bolus arrival times. The generalized mamillary model can be used to study possible kinetic heterogeneity in tumors, and does not assume instantaneous diffusion in the interacting compartments. The advantages of the proposed regression regularization approach for SVD deconvolution, as compared with previous methods, include its efficiency of computation, the ability to achieve adequate regularization to reproduce less noisy solutions, and that it does not require prior knowledge of the noise condition. 2008-09-17T09:15:39Z 2008-09-17T09:15:39Z 2003 2003 Research Report http://hdl.handle.net/10356/2853 Nanyang Technological University application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Koh, Tong San. Dynamic physiological imaging and quantification of tumor microcirculation |
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In this project, we have developed parametric and non-parametric methods for analysis of Dynamic Contrast-Enhanced (DCE) imaging data, with the aim of studying tumor microcirculation. These include a generalized mamillary distributed- parameter model for capillary-tissue exchange, a regression approach to regularization using both conventional and generalized Singular Value Decomposition (SVD), and the use of piecewise continuous regression models for the automatic estimation of bolus arrival times. The generalized mamillary model can be used to study possible kinetic heterogeneity in tumors, and does not assume instantaneous diffusion in the interacting compartments. The advantages of the proposed regression regularization approach for SVD deconvolution, as compared with previous methods, include its efficiency of computation, the ability to achieve adequate regularization to reproduce less noisy solutions, and that it does not require prior knowledge of the noise condition. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Koh, Tong San. |
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Research Report |
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Koh, Tong San. |
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Koh, Tong San. |
title |
Dynamic physiological imaging and quantification of tumor microcirculation |
title_short |
Dynamic physiological imaging and quantification of tumor microcirculation |
title_full |
Dynamic physiological imaging and quantification of tumor microcirculation |
title_fullStr |
Dynamic physiological imaging and quantification of tumor microcirculation |
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Dynamic physiological imaging and quantification of tumor microcirculation |
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dynamic physiological imaging and quantification of tumor microcirculation |
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2008 |
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http://hdl.handle.net/10356/2853 |
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