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Inversion recovery TrueFISP: quantification of T(1), T(2), and spin density.
Peter Schmitt1, Mark A Griswold, Peter M Jakob
1Experimentelle Physik 5, Physikalisches Institut, Universität Würzburg, Germany. ps@physik.uni-wuerzburg.de
Magnetic Resonance in Medicine
|April 6, 2004
Summary
This study introduces a new method to derive T(1), T(2) relaxation times, and spin density from TrueFISP MRI sequences. The technique accurately quanties these parameters, enhancing quantitative MRI capabilities.
Area of Science:
- Magnetic Resonance Imaging (MRI)
- Biophysics
- Medical Physics
Background:
- TrueFISP (True Fast Imaging with Steady-state Precession) is a widely used MRI sequence.
- Quantitative MRI parameter mapping (T(1), T(2), spin density) is crucial for accurate tissue characterization.
- Existing methods for quantitative TrueFISP parameter extraction can be complex or limited.
Purpose of the Study:
- To develop a novel procedure for extracting T(1), T(2), and relative spin density from TrueFISP signal time courses.
- To validate the proposed method using phantom and human volunteer data.
Main Methods:
- Utilizing a series of TrueFISP images acquired after spin inversion.
- Modeling magnetization recovery with a three-parameter monoexponential function: S(t) = S(stst)(1-INV exp(-t/T(*) (1)).
- Deriving T(1), T(2), and relative spin density from fit parameters, including the inversion factor (INV).
Main Results:
- The ratio T(1)/T(2) can be directly extracted from the inversion factor (INV).
- Analytical expressions enable direct derivation of T(1), T(2), and relative spin density from fit parameters.
- Phantom studies demonstrated excellent agreement with reference measurements.
- Human volunteer studies yielded T(1), T(2), and spin density maps consistent with literature values.
Conclusions:
- The proposed novel procedure accurately extracts T(1), T(2), and relative spin density from TrueFISP sequences.
- This method enhances the quantitative capabilities of TrueFISP MRI.
- The findings support the clinical utility of this approach for improved tissue characterization.