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Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps

Author

Listed:
  • Anna Tietze
  • Anne Nielsen
  • Irene Klærke Mikkelsen
  • Mikkel Bo Hansen
  • Annette Obel
  • Leif Østergaard
  • Kim Mouridsen

Abstract

Purpose: The purpose of this work is to investigate if the curve-fitting algorithm in Dynamic Contrast Enhanced (DCE) MRI experiments influences the diagnostic quality of calculated parameter maps. Material and methods: We compared the Levenberg-Marquardt (LM) and a Bayesian method (BM) in DCE data of 42 glioma patients, using two compartmental models (extended Toft’s and 2-compartment-exchange model). Logistic regression and an ordinal linear mixed model were used to investigate if the image quality differed between the curve-fitting algorithms and to quantify if image quality was affected for different parameters and algorithms. The diagnostic performance to discriminate between high-grade and low-grade gliomas was compared by applying a Wilcoxon signed-rank test (statistical significance p>0.05). Two neuroradiologists assessed different qualitative imaging features. Results: Parameter maps based on BM, particularly those describing the blood-brain barrier, were superior those based on LM. The image quality was found to be significantly improved (p

Suggested Citation

  • Anna Tietze & Anne Nielsen & Irene Klærke Mikkelsen & Mikkel Bo Hansen & Annette Obel & Leif Østergaard & Kim Mouridsen, 2018. "Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0202906
    DOI: 10.1371/journal.pone.0202906
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