Hamilton, Gavin

PurposeChemical shift‐encoded MRI (CSE‐MRI) is well‐established to quantify proton density fat fraction (PDFF) as a quantitative biomarker of hepatic steatosis. However, temperature is known to bias PDFF estimation in phantom studies. In this study, strategies were developed and evaluated to correct for the effects of temperature on PDFF estimation through simulations, temperature‐controlled experiments, and a multi‐center, multi‐vendor phantom study.Theory and MethodsA technical solution that assumes and automatically estimates a uniform, global temperature throughout the phantom is proposed. Computer simulations modeled the effect of temperature on PDFF estimation using magnitude‐, complex‐, and hybrid‐based CSE‐MRI methods. Phantom experiments were performed to assess the temperature correction on PDFF estimation at controlled phantom temperatures. To assess the temperature correction method on a larger scale, the proposed method was applied to data acquired as part of a nine‐site multi‐vendor phantom study and compared to temperature‐corrected PDFF estimation using an a priori guess for ambient room temperature.ResultsSimulations and temperature‐controlled experiments show that as temperature deviates further from the assumed temperature, PDFF bias increases. Using the proposed correction method and a reasonable a priori guess for ambient temperature, PDFF bias and variability were reduced using magnitude‐based CSE‐MRI, across MRI systems, field strengths, protocols, and varying phantom temperature. Complex and hybrid methods showed little PDFF bias and variability both before and after correction.ConclusionCorrection for temperature reduces temperature‐related PDFF bias and variability in phantoms across MRI vendors, sites, field strengths, and protocols for magnitude‐based CSE‐MRI, even without a priori information about the temperature.

Magnetic resonance elastography (MRE) is a promising technique for noninvasive assessment of fibrosis, a major determinant of outcome in nonalcoholic fatty liver disease (NAFLD). However, data in children are limited. The purpose of this study was to determine the accuracy of MRE for the detection of fibrosis and advanced fibrosis in children with NAFLD and to assess agreement between manual and novel automated reading methods. We performed a prospective, multicenter study of two‐dimensional (2D) MRE in children with NAFLD. MR elastograms were analyzed manually at two reading centers, and using a new automated technique. Analysis using each approach was done independently. Correlations were determined between MRE analysis methods and fibrosis stage. Thresholds for classifying the presence of fibrosis and of advanced fibrosis were computed and cross‐validated. In 90 children with a mean age of 13.1 ± 2.4 years, median hepatic stiffness was 2.35 kPa. Stiffness values derived by each reading center were strongly correlated with each other (r = 0.83). All three analyses were significantly correlated with fibrosis stage (center 1, ρ = 0.53; center 2, ρ = 0.55; and automated analysis, ρ = 0.52; P < 0.001). Overall cross‐validated accuracy for detecting any fibrosis was 72.2% for all methods (95% confidence interval [CI], 61.8%‐81.1%). Overall cross‐validated accuracy for assessing advanced fibrosis was 88.9% (95% CI, 80.5%‐94.5%) for center 1, 90.0% (95% CI, 81.9%‐95.3%) for center 2, and 86.7% (95% CI, 77.9%‐92.9%) for automated analysis. Conclusion : 2D MRE can estimate hepatic stiffness in children with NAFLD. Further refinement and validation of automated analysis techniques will be an important step in standardizing MRE. How to best integrate MRE into clinical protocols for the assessment of NAFLD in children will require prospective evaluation. (H epatology 2017;66:1474–1485)