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Simultaneous field and R2 mapping to quantify liver iron content using autoregressive moving average modeling.

Brian A Taylor¹, Ralf B Loeffler, Ruitian Song

¹Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.

Journal of Magnetic Resonance Imaging : JMRI

|December 20, 2011

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This study shows that autoregressive moving average (ARMA) modeling of complex multigradient echo (mGRE) MRI data allows simultaneous liver iron content (LIC) measurement using both susceptibility and R2 methods, aiding iron overload assessment.

Area of Science:

Magnetic Resonance Imaging (MRI)
Medical Physics
Radiology

Background:

Iron overload disorders require accurate liver iron content (LIC) assessment.
Traditional methods for LIC measurement can be invasive or less precise.
Advanced MRI techniques offer non-invasive alternatives for quantifying tissue iron.

Purpose of the Study:

To evaluate the efficacy of a complex multigradient echo (mGRE) acquisition combined with an autoregressive moving average (ARMA) model.
To enable simultaneous susceptibility and R2-based measurements for assessing liver iron content (LIC).
To investigate the utility of these simultaneous measurements in patients with iron overload.

Main Methods:

Fifty MRI exams with mGRE images (magnitude and phase) were analyzed.

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The ARMA model was employed to generate fat-separated field maps, R2 maps, and T1-weighted images.

LIC was calculated using liver-to-muscle susceptibility differences and R2 values, with linear regression analysis to compare methods.

Main Results:

LIC measurements derived from R2 showed a high correlation with susceptibility-based LIC (R² = 0.94).
Field inhomogeneity within the liver strongly correlated with R2 values (R² = 0.85).
The ARMA model successfully facilitated simultaneous susceptibility and R2-based LIC quantification.

Conclusions:

The ARMA model applied to complex mGRE images allows for simultaneous susceptibility and R2-based LIC assessment.
Susceptibility measurements can serve as a validation tool for R2-based LIC assessments in iron overload.
This integrated approach enhances the accuracy and reliability of non-invasive iron overload evaluation.