Relaxivity-iron calibration in hepatic iron overload: Predictions of a Monte Carlo model

Magn Reson Med. 2015 Sep;74(3):879-83. doi: 10.1002/mrm.25459. Epub 2014 Sep 19.

Abstract

Purpose: R2* (1/T2*) and single echo R2 (1/T2) have been calibrated to liver iron concentration (LIC) in patients with thalassemia and transfusion-dependent sickle cell disease at 1.5T. The R2*-LIC relationship is linear, whereas that of R2 is curvilinear. However, the increasing popularity of high-field scanners requires generalizing these relationships to higher field strengths. In this study, we tested the hypothesis that numerical simulation can accurately determine the field dependence of iron-mediated transverse relaxation rates.

Methods: We previously replicated the calibration curves between R2 and R2* and iron at 1.5T using Monte Carlo models incorporating realistic liver structure, iron deposit susceptibility, and proton mobility. In this paper, we extend our model to predict relaxivity-iron calibrations at higher field strengths. Predictions were validated by measuring R2 and R2* at 1.5T and 3T in six β-thalassemia major patients.

Results: Predicted R2* increased twofold at 3T from 1.5T, whereas R2 increased by a factor of 1.47. Patient data exhibited a coefficient of variation of 3.6% and 7.2%, respectively, to the best-fit simulated data. Simulations over the range 0.25T-7T showed R2* increasing linearly with field strength, whereas R2 exhibited a concave-downward relationship.

Conclusion: A model-based approach predicts alterations in relaxivity-iron calibrations with field strength without repeating imaging studies. The model may generalize to alternative pulse sequences and tissue iron distribution.

Keywords: 3T; Monte Carlo; high field; iron; liver; relaxation; relaxivity.

MeSH terms

  • Calibration
  • Humans
  • Iron / analysis*
  • Iron Overload / physiopathology*
  • Liver / chemistry*
  • Liver / physiology
  • Magnetic Resonance Imaging / methods*
  • Magnetic Resonance Imaging / statistics & numerical data
  • Models, Biological*
  • Monte Carlo Method

Substances

  • Iron