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Radiology. 2018 Feb;286(2):486-498. doi: 10.1148/radiol.2017170550. Epub 2017 Sep 11.

Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis.

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From the Department of Radiology, Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Deptartment of Radiology, Cincinnati Children's Hospital and Medical Center, Cincinnati, Ohio (S.D.S.); Department of Radiology and Center for Advanced Magnetic Resonance Development, Duke University Medical Center, Durham, NC (M.R.B.); Department of Radiology and Liver Imaging Group, University of California at San Diego, San Diego, Calif (G.H., M.S.M., C.B.S.); Division of Gastroenterology, Department of Medicine, University of California, San Diego, La Jolla, Calif (R.L.); Division of Epidemiology, Department of Family Medicine and Preventive Medicine, University of California, San Diego, La Jolla, Calif (R.L.); Departments of Radiology and Medical Physics, University of Wisconsin, Madison, Wis (D.H., S.B.R.); Biomedical Engineering, Medicine and Emergency Medicine, University of Wisconsin, Madison, Wis (S.B.R.); Department of Radiology, Phoenix Children's Hospital, Phoenix, Ariz (H.H.H.); Institute of Clinical Radiology, Hospital of the Ludwig-Maximilian University, Munich, Germany (H.H.); Institute of Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany (J.P.K.); Department of Radiology, University Hospital, Carl Gustav Carus University, Dresden, Germany (J.P.K.); Department of Radiology, Rheinische Friedrich-Wilhelms Universität, Bonn, Germany (G.M.K.); Quantitative Health Sciences, The Cleveland Clinic Foundation, Cleveland, Ohio (N.A.O.); Department of Radiology, Chonbuk National University Medical School and Hospital, Jeonju, Chonbuk, Korea (J.S.S.); Department of Radiology, Université de Montréal, Montréal, QC, Canada (A.T.); and Department of Radiology, Beijing Military General Hospital, Beijing, China (X.W.).


Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017.

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