Dynamic contrast‐enhanced MRI diagnostics in oncology via principal component analysis Conferences

abstract

• AbstractThis paper proposes the use of latent variable models based on principal component analysis (PCA) as a robust alternative to pharmacokinetic modeling for the analysis of the dynamic contrast‐enhanced magnetic resonance images (DCE‐MRI) often obtained during oncological studies. Theoretical and practical justifications are provided for the advantages of the PCA approach. A pilot study using clinical DCE‐MRI data on prostate patients is presented to demonstrate the method. Copyright © 2008 John Wiley & Sons, Ltd.

authors

• Bruwer, Mark-John
• Macgregor, John Frederick
• Noseworthy, Michael

status

• published 

publication date

• November 2008

has subject area

• 0301 Analytical Chemistry (FoR)
• Analytical Chemistry (Science Metrix)

published in

• Journal of Chemometrics  Journal

presented at event

• 10th Scandinavian Symposium on Chemometrics  Conference

keywords

• Automation & Control Systems
• Chemistry
• Chemistry, Analytical
• Computer Science
• Computer Science, Artificial Intelligence
• DCE-MRI
• DISCRIMINATION
• GLAND
• Instruments & Instrumentation
• Mathematics
• Mathematics, Interdisciplinary Applications
• PCA
• PERMEABILITY
• PROSTATE-CANCER
• Physical Sciences
• Science & Technology
• Statistics & Probability
• TRACER KINETICS
• Technology
• latent variable modeling (LVM)
• multivariate image analysis (MIA)
• oncology

Digital Object Identifier (DOI)

• 10.1002/cem.1143

start page

• 708

end page

• 716

volume

• 22

issue

• 11-12