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Inflow effect correction in fast gradient-echo perfusion imaging.

Marko K Ivancevic1, Ivan Zimine, Xavier Montet

  • 1UIN, Department of Radiology, Geneva University Hospital, Geneva, Switzerland. marko.ivancevic@physics.unige.ch

Magnetic Resonance in Medicine
|October 31, 2003
PubMed
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This study addresses the inflow effect on MRI signal intensity during perfusion imaging. A new flow-corrected calibration method improves arterial input function accuracy for better tissue perfusion quantification.

Area of Science:

  • Medical Imaging
  • Biophysics
  • Pharmacokinetics

Background:

  • Fast gradient-recalled-echo (GRE) sequences are crucial for observing first-pass perfusion in MRI.
  • The inflow effect can significantly impact signal intensity (SI) and accuracy in perfusion imaging.
  • Accurate quantification of tissue perfusion relies on precise arterial input function (AIF) determination.

Purpose of the Study:

  • To assess the extent of the inflow effect on SI in fast GRE perfusion sequences.
  • To develop and validate a method for correcting the inflow effect.
  • To improve the accuracy and robustness of AIF determination for MRI-based tissue perfusion quantification.

Main Methods:

  • A phantom experiment with a flow apparatus was used to determine SI as a function of Gd-DTPA concentration and velocity.

Related Experiment Videos

  • A flow-sensitive calibration method was developed.
  • The method was validated using bolus injections in an open-circuit flow apparatus and in vivo.

    • Main Results:

    • Static phantom calibration methods are inadequate for accurate signal-to-concentration conversion in high-flow scenarios.
    • The developed flow-corrected calibration method demonstrated improved accuracy.
    • The method enhances the robustness of AIF determination in perfusion imaging.

    Conclusions:

    • The inflow effect significantly influences SI in fast GRE perfusion imaging.
    • A novel flow-corrected calibration method is effective in mitigating the inflow effect.
    • This approach improves the reliability of MRI-based tissue perfusion quantification using contrast media.