Cart (Loading....) | Create Account
Close category search window
 

Convex analysis for separation of functional patterns in DCE-MRI: A longitudinal study to antiangiogenic therapy

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Tsung-Han Chan ; Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Arlington, VA ; Li Chen ; Choyke, P.L. ; Chong-Yung Chi
more authors

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can characterize vascular heterogeneity, and has potential utility in assessment of the efficacy of angiogenesis inhibitors in cancer treatment. Due to the heterogeneous nature of tumor microvasculature, the measured signals can be represented as the mixture of the permeability images corresponding to different perfusion rates. We recently reported a hybrid convex analysis of mixture framework for unmixing of non-negative yet dependent angiogenic permeability distributions (APDs) and perfusion time activity curves (TACs). In our last work, we presented an underlying theory to infer the concept that the TACs can be identified by finding the lateral edges of an observation-constructed convex pyramid when the well-grounded points exist for all APDs. For fulfilling this concept, a hybrid method including non-negative clustered component analysis, convex analysis, and least-squares fitting with non-negativity constraints was developed. In this paper, we use computer simulations to validate the performance of our reported framework, and further apply it to three sets of real DCE-MRI data, before and during the treatment period, for assessing the response to antiangiogenic therapy. The experimental results are not only surprisingly meaningful in biology and clinic, but also capable of reflecting the efficacy of angiogenesis inhibitors in cancer treatment.

Published in:

Machine Learning for Signal Processing, 2008. MLSP 2008. IEEE Workshop on

Date of Conference:

16-19 Oct. 2008

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.