Autoregressive Moving Average Modeling for Hepatic Iron Quantification in the Presence of Fat

Overview

Journal J Magn Reson Imaging

Date 2019 Feb 15

PMID 30761652

Citations 5

Authors

Aaryani Tipirneni-Sajja

Axel J Krafft

Ralf B Loeffler

Ruitian Song

Armita Bahrami

Jane S Hankins

Claudia M Hillenbrand

Affiliations

Soon will be listed here.

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 ) 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.

Citing Articles

Simulation of a virtual liver iron overload model and R * estimation using multispectral fat-water models for GRE and UTE acquisitions at 1.5 T and 3 T.

Neupane P, Shrestha U, Brasher S, Abramson Z, Tipirneni-Sajja A NMR Biomed. 2023; 36(12):e5018.

PMID: 37539770 PMC: 10838367. DOI: 10.1002/nbm.5018.

Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms.

Tipirneni-Sajja A, Brasher S, Shrestha U, Johnson H, Morin C, Satapathy S MAGMA. 2022; 36(4):529-551.

PMID: 36515810 DOI: 10.1007/s10334-022-01053-z.

Hepatic Iron Quantification Using a Free-Breathing 3D Radial Gradient Echo Technique and Validation With a 2D Biopsy-Calibrated R Relaxometry Method.

Rohani S, Morin C, Zhong X, Kannengiesser S, Shrestha U, Goode C J Magn Reson Imaging. 2021; 55(5):1407-1416.

PMID: 34545639 PMC: 10424632. DOI: 10.1002/jmri.27921.

Quantitative Susceptibility Mapping Using a Multispectral Autoregressive Moving Average Model to Assess Hepatic Iron Overload.

Tipirneni-Sajja A, Loeffler R, Hankins J, Morin C, Hillenbrand C J Magn Reson Imaging. 2021; 54(3):721-727.

PMID: 33634923 PMC: 9223690. DOI: 10.1002/jmri.27584.

Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China.

Zheng Y, Zhang X, Wang X, Wang K, Cui Y BMJ Open. 2021; 11(1):e041040.

PMID: 33478962 PMC: 7825257. DOI: 10.1136/bmjopen-2020-041040.

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