Autoregressive moving average modeling for hepatic iron quantification in the presence of fat

Measuring hepatic R2* by fitting a monoexponential model to the signal decay of a multigradient-echo (mGRE) sequence noninvasively determines hepatic iron content (HIC). Concurrent hepatic steatosis introduces signal oscillations and confounds R2* quantification with standard monoexponential models.

To evaluate an autoregressive moving average (ARMA) model for accurate quantification of HIC in the presence of fat using biopsy as the reference.

Phantom study and in vivo cohort.

Twenty iron-fat phantoms covering clinically relevant R2* (30-800 s^-1 ) and fat fraction (FF) ranges (0-40%), and 10 patients (four male, six female, mean age 18.8 years).

2D mGRE acquisitions at 1.5 T and 3 T.

Phantoms were scanned at both field strengths. In vivo data were analyzed using the ARMA model to determine R2* and FF values, and compared with biopsy results.

Linear regression analysis was used to compare ARMA R2* and FF results with those obtained using a conventional monoexponential model, complex-domain nonlinear least squares (NLSQ) fat-water model, and biopsy.

In phantoms and in vivo, all models produced R2* and FF values consistent with expected values in low iron and low/high fat conditions. For high iron and no fat phantoms, monoexponential and ARMA models performed excellently (slopes: 0.89-1.07), but NLSQ overestimated R2* (slopes: 1.14-1.36) and produced false FFs (12-17%) at 1.5 T; in high iron and fat phantoms, NLSQ (slopes: 1.02-1.16) outperformed monoexponential and ARMA models (slopes: 1.23-1.88). The results with NLSQ and ARMA improved in phantoms at 3 T (slopes: 0.96-1.04). In patients, mean R2*-HIC estimates for monoexponential and ARMA models were close to biopsy-HIC values (slopes: 0.90-0.95), whereas NLSQ substantially overestimated HIC (slope 1.4) and produced false FF values (4-28%) with very high SDs (15-222%) in patients with high iron overload and no steatosis.

ARMA is superior in quantifying R2* and FF under high iron and no fat conditions, whereas NLSQ is superior for high iron and concurrent fat at 1.5 T. Both models give improved R2* and FF results at 3 T.

2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1620-1632.