ArticleJournal of magnetic resonance imaging : JMRI2019
Autoregressive moving average modeling for hepatic iron quantification in the presence of fat.
Article in Journal of magnetic resonance imaging : JMRI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Evaluation of 0.55T MRI for Simultaneous R2* and Fat Fraction Quantification in the Presence of Hepatic Iron Overload and Steatosis Using Simulations and Phantoms.Magnetic resonance in medicine · 2026Article
- Impact of particle size onMagnetic resonance in medicine · 2025Article
- Hepatic steatosis modeling and MRI signal simulations for comparison of single- and dual-R2* models and estimation of fat fraction at 1.5T and 3T.Computers in biology and medicine · 2024Article
- Simulation of a virtual liver iron overload model and RNMR in biomedicine · 2023Article
- Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms.Magma (New York, N.Y.) · 2023Review
- Hepatic Iron Quantification Using a Free-Breathing 3D Radial Gradient Echo Technique and Validation With a 2D Biopsy-Calibrated RJournal of magnetic resonance imaging : JMRI · 2022Article
- Quantitative Susceptibility Mapping Using a Multispectral Autoregressive Moving Average Model to Assess Hepatic Iron Overload.Journal of magnetic resonance imaging : JMRI · 2021Article
- Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China.BMJ open · 2021Article
- A hybrid (iron-fat-water) phantom for liver iron overload quantification in the presence of contaminating fat using magnetic resonance imaging.Magma (New York, N.Y.) · 2020Article
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundMeasuring 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 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.
resultsIn 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
conclusionARMA 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.