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.

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

“…Many studies have found that Box-Jenkins method has a good ability of fitting and forecasting. For stationary time series that do not contain seasonality, it is more suitable to use the ARMA model of the Box-Jenkins method to do prediction analysis, 35 for non-stationary time series of infectious diseases with obvious seasonality, it is more suitable to use the seasonal autoregressive integrated moving average (SARIMA) model of the Box-Jenkins method for prediction analysis. [9][10][11][12] In our study, from figure 3, we could see that the seasonality of the TB incidence in Kashgar from 2005 to 2014 was not obvious, there was only a certain seasonality from 2015 to 2017, and we found that the time series of TB incidence was stable by the ADF unit root test, and the autocorrelation and partial correlation coefficients of modelling data at lag 12, 24 were not obviously large, therefore, for our research data, we used the ARMA model to do forecast analysis, and finally, we established the AR ((1, 2, 8)) model of the Box-Jenkins method with its good performance in fitting and predicting the TB incidence of Kashgar in Xinjiang.…”

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

“…Any inaccurate assumptions in the signal model will corrupt the field map estimations and thereby the QSM maps. In contrast, ARMA does not require prior information about the relative amplitudes and frequencies of the lipid peaks and provides separate R2* values for water and fat species 14,29 . However, additional studies are still needed to investigate whether high HIC affects lipid chemical shifts and R2* decay rates differently for water and fat species and causes bias in R2*, FF, and field map estimations.…”

Morphological characterization of hepatic steatosis and Monte Carlo modeling of MRI signal for accurate quantification of fat fraction and relaxivity

“…The ARMA model was implemented as an iterative approach, starting with the maximum number of peaks (six lipid peaks and one water peak) and reducing the number of peaks iteratively until the frequencies of the detected lipid peaks fell within the range of the reported relative frequencies (±0.5 ppm), as previously described. 25 The dual-R 2 * model calculated separate R 2 * values for water (referred to as just R 2 *) and for the main fat peak (referred to as fat R 2 *). For both single-and dual-R 2 * models, R 2 *-FF relationships were obtained using linear regression analysis.…”

“…This might be because the dual-R 2 * model used does not use prior knowledge of the relative fat amplitudes and frequencies as the single-R 2 * model does, and thus would require a sufficiently high signal to localize the individual water and fat peaks. 25 The dual-R 2 * model was able to identify only a total of three peaks (one water peak and two fat peaks); hence, the inability to accurately identify all the peaks might have caused some bias in estimating FF values.…”

“…[21][22][23][24][25] In simulation studies, the sizes and distribution of fat molecules were not considered, 23 and in phantom studies the sizes of fat droplets (FDs) were not controlled to match the scales of in vivo fat deposits. 22,25 Hence, there is a gap in our understanding of how the true microscopic arrangement of fat deposits in vivo can cause susceptibility-induced inhomogeneities and affect the macroscopic MRI signal relaxation of water and fat protons. Further, in vivo studies have shown that R 2 * increases with FF, even when the signal model includes the presence of fat, but the mechanism underlying this behavior remains unclear.…”