Dataset Information

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.

SUBMITTER: Tipirneni-Sajja A

PROVIDER: S-EPMC6785364 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Tipirneni-Sajja Aaryani A Krafft Axel J AJ Loeffler Ralf B RB Song Ruitian R Bahrami Armita A Hankins Jane S JS Hillenbrand Claudia M CM

Journal of magnetic resonance imaging : JMRI 20190213 5

<h4>Background</h4>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.<h4>Purpose</h4>To evaluate an autoregressive moving average (ARMA) model for accurate quantification of HIC in the presence of fat using biopsy as the reference.<h4>Study type</h4> ...[more]

PMID: 30761652

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Dataset Information

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

Publications

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

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