Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA.
J Magn Reson Imaging. 2019 Nov;50(5):1620-1632. doi: 10.1002/jmri.26682. Epub 2019 Feb 13.
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.
To evaluate an autoregressive moving average (ARMA) model for accurate quantification of HIC in the presence of fat using biopsy as the reference.
Phantom study and in vivo cohort.
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.
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.
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.
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.
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.
2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1620-1632.
通过拟合多梯度回波（mGRE）序列信号衰减的单指数模型，无创测量肝脏 R2可确定肝脏铁含量（HIC）。同时存在的肝脂肪变性会引入信号波动，并使用标准单指数模型对 R2的定量产生干扰。
评估自回归移动平均（ARMA）模型在存在脂肪的情况下，通过活检作为参考，对 HIC 进行准确定量的能力。
体模研究和体内队列研究。
涵盖临床相关 R2*（30-800 s）和脂肪分数（FF）范围（0-40%）的 20 个铁脂肪体模，以及 10 名患者（4 名男性，6 名女性，平均年龄 18.8 岁）。
场强/序列：1.5 T 和 3 T 的 2D mGRE 采集。
在两个场强下对体模进行扫描。使用 ARMA 模型对体内数据进行分析，以确定 R2*和 FF 值，并与活检结果进行比较。
使用线性回归分析比较 ARMA R2*和 FF 结果与使用传统单指数模型、复域非线性最小二乘法（NLSQ）脂肪-水模型和活检获得的结果。
在体模和体内，所有模型在低铁和高低脂肪条件下产生的 R2和 FF 值与预期值一致。对于高铁和无脂肪体模，单指数和 ARMA 模型表现出色（斜率：0.89-1.07），但 NLSQ 高估了 R2（斜率：1.14-1.36），并在 1.5 T 时产生错误的 FF（12-17%）；在高铁和脂肪体模中，NLSQ（斜率：1.02-1.16）优于单指数和 ARMA 模型（斜率：1.23-1.88）。在 3 T 时，NLSQ 和 ARMA 的结果在体模中得到改善（斜率：0.96-1.04）。在患者中，单指数和 ARMA 模型的平均 R2*-HIC 估计值与活检-HIC 值接近（斜率：0.90-0.95），而 NLSQ 则大大高估了 HIC（斜率 1.4），并在没有铁过载和脂肪变性的患者中产生错误的 FF 值（4-28%），其标准差非常高（15-222%）。
ARMA 在高铁和无脂肪条件下量化 R2和 FF 更具优势，而 NLSQ 在 1.5 T 时适用于高铁和并发脂肪。两种模型在 3 T 时都能得到更好的 R2和 FF 结果。
2 技术功效阶段：2 J. Magn. Reson. Imaging 2019;50:1620-1632.
