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Related Experiment Video

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DCE-MRI pixel-by-pixel quantitative curve pattern analysis and its application to osteosarcoma.

Jun-Yu Guo1, Wilburn E Reddick

  • 1Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee 38105-3678, USA. junyu.guo@stjude.org

Journal of Magnetic Resonance Imaging : JMRI
|June 27, 2009
PubMed
Summary

A new curve pattern analysis (CPA) method for dynamic contrast-enhanced MRI (DCE-MRI) offers a repeatable way to quantify signals. This method minimizes variations from arterial input function and T(1) measurements, providing a feasible alternative for DCE-MRI studies.

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Area of Science:

  • Medical Imaging
  • Radiology
  • Biophysics

Background:

  • Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is crucial for characterizing tissue perfusion and vascularity.
  • Quantifying DCE-MRI data often relies on pharmacokinetic models requiring arterial input function (AIF) and T(1) measurements, which can introduce variability.
  • Developing robust methods for DCE-MRI analysis is essential for accurate disease assessment.

Purpose of the Study:

  • To introduce a novel curve pattern analysis (CPA) method for DCE-MRI signal quantification.
  • To characterize and quantify DCE-MRI signal curves without needing AIF or T(1) measurements.
  • To evaluate the performance and reliability of the CPA method.

Main Methods:

  • The CPA method analyzes characteristics of scaled DCE signal curves.

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  • Simulations were conducted to assess CPA parameter dependency on T(1), repetition time (TR), and flip angle.
  • In vivo studies involved five pediatric osteosarcoma patients, comparing CPA-generated parametric maps with those from a pharmacokinetic model.

    • Main Results:

    • CPA parameters demonstrated minimal variation (<2% for T(1) changes, <10% for flip angle changes) in simulations.
    • Qualitative identification of various DCE-MRI curve patterns was achievable using CPA parameter maps.
    • Strong correlations were observed between the CPA parameter and the pharmacokinetic parameter k(ep) (R²=0.9983 in simulations, R²=0.95 in vivo).

    Conclusions:

    • A novel CPA method for DCE-MRI analysis has been successfully developed.
    • The CPA method offers a feasible and potentially more repeatable alternative for quantifying DCE-MRI data.
    • This approach mitigates variability associated with AIF and T(1) estimations and model dependence.