Dynamic physiological imaging and quantification of tumor microcirculation

• Main Author: Koh, Tong San.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Research Report
• Published: 2008
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
• Online Access: http://hdl.handle.net/10356/2853
• Institution: Nanyang Technological University

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.