Dynamic contrast-enhanced MRI diagnostics in oncology via principal component analysis

Authors

  • Mark-John Bruwer,

    1. Department of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4L7
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  • John F. MacGregor,

    Corresponding author
    1. Department of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4L7
    • Department of Chemical Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4L7
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  • Michael D. Noseworthy

    1. Brain-Body Institute, St. Joseph's Healthcare, 50 Charlton Avenue East, Hamilton, Ontario, Canada L8N 4A6
    2. Department of Biomedical Engineering, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4L7
    3. Department of Medical Physics and Applied Radiation Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario, Canada L8S 4L7
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Abstract

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.

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