Assessment of tumor blood flow distribution by dynamic contrast-enhanced CT

• Main Authors: Koh, Tong San, Shi, Wen, Thng, Choon Hua, Ho, Juliana Teng Swan, Khoo, James Boon Kheng, Cheong, Dennis Lai Hong, Lim, Tchoyoson C. C.
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Electrical and electronic engineering; Cerebral Tumors; Perfusion Imaging
• Online Access: https://hdl.handle.net/10356/106005; http://hdl.handle.net/10220/48893; https://doi.org/10.1109/TMI.2013.2258404
• Institution: Nanyang Technological University

A distinct feature of the tumor vasculature is its tortuosity and irregular branching of vessels, which can translate to a wider dispersion and higher variability of blood flow in the tumor. To enable tumor blood flow variability to be assessed in vivo by imaging, a tracer kinetic model that accounts for flow dispersion is developed for use with dynamic contrast-enhanced (DCE) CT. The proposed model adopts a multiple-pathway approach and allows for the quantification of relative dispersion in the blood flow distribution, which reflects flow variability in the tumor vasculature. Monte Carlo simulation experiments were performed to study the possibility of reducing the number of model parameters based on the Akaike information criterion approach and to explore possible noise and tissue conditions in which the model might be applicable. The model was used for region-of-interest analysis and to generate perfusion parameter maps for three patient DCE CT cases with cerebral tumors, to illustrate clinical applicability.