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Dynamic contrast‐enhanced MRI diagnostics in oncology via principal component analysis
Author(s) -
Bruwer MarkJohn,
MacGregor John F.,
Noseworthy Michael D.
Publication year - 2008
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1143
Subject(s) - principal component analysis , dynamic contrast , contrast (vision) , magnetic resonance imaging , dynamic contrast enhanced mri , computer science , artificial intelligence , medical physics , pattern recognition (psychology) , nuclear medicine , medicine , radiology
This 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.