Three-dimensional balanced steady state free precession imaging of the prostate: flip angle dependency of the signal based on a two component T2-decay model

J Magn Reson Imaging. 2010 May;31(5):1124-31. doi: 10.1002/jmri.22135.

Abstract

Purpose: To investigate the contrast of three-dimensional balanced steady state free precession (3D bSSFP) in the two component T2 model and to apply the results to optimize 3D bSSFP for prostate imaging at 1.5 Tesla.

Materials and methods: In each of seven healthy volunteers, six 3D bSSFP acquisitions were performed with flip angles (alpha) equally spaced between 10 degrees and 110 degrees . Predictions of signal and contrast were obtained from synthetic bSSFP images calculated from relaxation parameters obtained from a multi-spin-echo acquisition. One biexponential and two monoexponential models were applied. Measured and predicted signals were compared by simple linear regression.

Results: The measured contrast to signal ratio increased continuously with alpha. Mean R(2) for the biexponential model was almost constant for alpha in the range 50-110 degrees . The biexponential model was a better predictor of the measured signal than the monoexponential model. A monoexponential model restricted to the echoes TE = 50-125 ms performed similar to the biexponential model. The predicted contrast peaked at alpha between 50 degrees and 90 degrees .

Conclusion: Prostate imaging with bSSFP benefited from high flip angles. The biexponential model provided good signal prediction while predictions from the monoexponential models are dependent on the range of TE used for T2 determination.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms*
  • Computer Simulation
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Middle Aged
  • Models, Anatomic*
  • Models, Biological*
  • Models, Statistical
  • Prostate / anatomy & histology*
  • Reproducibility of Results
  • Sensitivity and Specificity