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

Abstract

Background: 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.

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

Study type: Phantom study and in vivo cohort.

Population: 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).

Field strength/sequence: 2D mGRE acquisitions at 1.5 T and 3 T.

Assessment: 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.

Statistical tests: 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.

Results: 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.

Data conclusion: 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.

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

Keywords: ARMA modeling; R2* quantification; fat fraction; hemosiderosis; hepatic iron overload; steatosis.