Comparison of apparent diffusion coefficients and distributed diffusion coefficients in high-grade gliomas

J Magn Reson Imaging. 2010 Mar;31(3):531-7. doi: 10.1002/jmri.22070.

Abstract

Purpose: To compare apparent diffusion coefficients (ADCs) with distributed diffusion coefficients (DDCs) in high-grade gliomas.

Materials and methods: Twenty patients with high-grade gliomas prospectively underwent diffusion-weighted MRI. Traditional ADC maps were created using b-values of 0 and 1000 s/mm(2). In addition, DDC maps were created by applying the stretched-exponential model using b-values of 0, 1000, 2000, and 4000 s/mm(2). Whole-tumor ADCs and DDCs (in 10(-3) mm(2)/s) were measured and analyzed with a paired t-test, Pearson's correlation coefficient, and the Bland-Altman method.

Results: Tumor ADCs (1.14 +/- 0.26) were significantly lower (P = 0.0001) than DDCs (1.64 +/- 0.71). Tumor ADCs and DDCs were strongly correlated (R = 0.9716; P < 0.0001), but mean bias +/- limits of agreement between tumor ADCs and DDCs was -0.50 +/- 0.90. There was a clear trend toward greater discordance between ADC and DDC at high ADC values.

Conclusion: Under the assumption that the stretched-exponential model provides a more accurate estimate of the average diffusion rate than the mono-exponential model, our results suggest that for a little diffusion attenuation the mono-exponential fit works rather well for quantifying diffusion in high-grade gliomas, whereas it works less well for a greater degree of diffusion attenuation.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Brain Neoplasms / pathology*
  • Brain Neoplasms / physiopathology*
  • Computer Simulation
  • Diffusion Magnetic Resonance Imaging / methods*
  • Female
  • Glioma / pathology*
  • Glioma / physiopathology*
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Male
  • Middle Aged
  • Models, Biological*
  • Reproducibility of Results
  • Sensitivity and Specificity