A general framework for hepatic iron overload quantification using MRI

Magnetic resonance imaging (MRI) has been considered for the quantification of iron overload in the liver. Iron overload was found to correlate with T2* measurement using T2* weighted images. In this work, we address the problem of iron overload estimation in the liver using MRI. We propose a general framework for all liver models proposed in the literature. The iron overload estimation task is then formulated as a minimization problem, and suitable regularization functions are assigned to the unknown model parameters. Subsequently, an alternating direction method of multipliers (ADMM) is used to estimate these unknown parameters. Three different models are derived, tested and compared; namely the single exponential (SEXP), the bi-exponential (BiEXP), and the exponential plus constant (CEXP). Simulations conducted using synthetic datasets indicate good correlation between estimated and ground truth T2* for all models. Moreover, the algorithms are evaluated using MRI scans of nine patients of different iron concentrations, using a 3-Tesla MRI scanner. The estimated T2* values of the proposed approaches are found to correlate with those obtained by the MRI scanner console. Moreover, the proposed approaches outperform several existing methods in the literature for iron overload estimation