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As the burden of liver disease reaches epidemic levels, there is a high unmet medical need to develop robust, accurate and reproducible non-invasive methods to quantify liver tissue characteristics for use in clinical development and ultimately in clinical practice. This prospective cross-sectional study systematically examines the repeatability and reproducibility of iron-corrected T1 (cT1), T2*, and hepatic proton density fat fraction (PDFF) quantification with multiparametric MRI across different field strengths, scanner manufacturers and models. 61 adult participants with mixed liver disease aetiology and those without any history of liver disease underwent multiparametric MRI on combinations of 5 scanner models from two manufacturers (Siemens and Philips) at different field strengths (1.5T and 3T). We report high repeatability and reproducibility across different field strengths, manufacturers, and scanner models in standardized cT1 (repeatability CoV 1.7%, bias -7.5ms, 95% LoA of -53.6 ms to 38.5 ms; reproducibility CoV 3.3%, bias 6.5 ms, 95% LoA of -76.3 to 89.2 ms) and T2* (repeatability CoV 5.5%, bias -0.18 ms, 95% LoA -5.41 to 5.05 ms; reproducibility CoV 6.6%, bias -1.7 ms, 95% LoA -6.61 to 3.15 ms) in human measurements. PDFF repeatability (0.8%) and reproducibility (0.75%) coefficients showed high precision of this metric. Similar precision was observed in phantom measurements. Inspection of the ICC model indicated that most of the variance in cT1 could be accounted for by study participants (ICC = 0.91), with minimal contribution from technical differences. We demonstrate that multiparametric MRI is a non-invasive, repeatable and reproducible method for quantifying liver tissue characteristics across manufacturers (Philips and Siemens) and field strengths (1.5T and 3T). [ABSTRACT FROM AUTHOR]

The burden of liver disease continues to increase in the UK, with liver cirrhosis reported to be the third most common cause of premature death. Iron overload, a condition that impacts liver health, was traditionally associated with genetic disorders such as hereditary haemochromatosis, however, it is now increasingly associated with obesity, type-2 diabetes and non-alcoholic fatty liver disease. The aim of this study was to assess the prevalence of elevated levels of liver iron within the UK Biobank imaging study in a cohort of 9108 individuals. Magnetic resonance imaging (MRI) was undertaken at the UK Biobank imaging centre, acquiring a multi-echo spoiled gradient-echo single-breath-hold MRI sequence from the liver. All images were analysed for liver iron and fat (expressed as proton density fat fraction or PDFF) content using LiverMultiScan. Liver iron was measured in 97.3% of the cohort. The mean liver iron content was 1.32 ± 0.32 mg/g while the median was 1.25 mg/g (min: 0.85 max: 6.44 mg/g). Overall 4.82% of the population were defined as having elevated liver iron, above commonly accepted 1.8 mg/g threshold based on biochemical iron measurements in liver specimens obtained by biopsy. Further analysis using univariate models showed elevated liver iron to be related to male sex (p<10−16, r2 = 0.008), increasing age (p<10−16, r2 = 0.013), and red meat intake (p<10−16, r2 = 0.008). Elevated liver fat (>5.6% PDFF) was associated with a slight increase in prevalence of elevated liver iron (4.4% vs 6.3%, p = 0.0007). This study shows that population studies including measurement of liver iron concentration are feasible, which may in future be used to better inform patient stratification and treatment. [ABSTRACT FROM AUTHOR]

Purpose: Several studies have demonstrated the accuracy, precision, and reproducibility of proton density fat fraction (PDFF) quantification using vendor-specific image acquisition protocols and PDFF estimation methods. The purpose of this work is to validate a confounder-corrected, cross-vendor, cross field-strength, in-house variant LMS IDEAL of the IDEAL method licensed from the University of Wisconsin, which has been developed for routine clinical use. Methods: LMS IDEAL is implemented using a combination of patented and/or published acquisition and some novel model fitting methods required to correct confounds which result from the imaging and estimation processes, including: water-fat ambiguity; T2* relaxation; multi-peak fat modelling; main field inhomogeneity; T1 and noise bias; bipolar readout gradients; and eddy currents. LMS IDEAL has been designed to use image acquisition protocols that can be installed on most MRI scanners and cloud-based image processing to provide fast, standardized clinical results. Publicly available phantom data were used to validate LMS IDEAL PDFF calculations against results from originally published IDEAL methodology. LMS PDFF and T2* measurements were also compared with an independent technique in human volunteer data (n = 179) acquired as part of the UK Biobank study. Results: We demonstrate excellent agreement of LMS IDEAL across vendors, field strengths, and over a wide range of PDFF and T2* values in the phantom study. The performance of LMS IDEAL was then assessed in vivo against widely accepted PDFF and T2* estimation methods (LMS Dixon and LMS T2*, respectively), demonstrating the robustness of LMS IDEAL to potential sources of error. Conclusion: The development and clinical validation of the LMS IDEAL algorithm as a chemical shift-encoded MRI method for PDFF and T2* estimation contributes towards robust, unbiased applications for quantification of hepatic steatosis and iron overload, which are key features of chronic liver disease. [ABSTRACT FROM AUTHOR]