Autoregressive moving average modeling for hepatic iron quantification in the presence of fat. Issue 5 (13th February 2019)

• Record Type: Journal Article
• Authors: Tipirneni‐Sajja, Aaryani; Krafft, Axel J.; Loeffler, Ralf B.; Song, Ruitian; Bahrami, Armita; Hankins, Jane S.; Hillenbrand, Claudia M.
• Is Part Of: Journal of magnetic resonance imaging. Volume 50:Issue 5(2019)
• Page Start: 1620
• Page End: 1632
• Publication Date: 2019-02-13
• Subjects: ARMA modeling -- R2* quantification -- hepatic iron overload -- hemosiderosis -- fat fraction -- steatosis; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.26682
• Languages: English
• ISSNs: 1053-1807

Abstract

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.