Hernando, Diego

ABSTRACT Background In chronic liver disease, fat and fibroinflammatory changes often coexist. However, their biomarkers, proton density fat fraction (PDFF) and T 1 , are typically assessed separately. Their reproducibility under mutual confounding remains unclear. Purpose To assess multicenter, multi‐vendor reproducibility of confounder‐corrected chemical shift‐encoded (CSE)‐MRI‐based PDFF mapping and MOLLI‐based T 1 mapping using a combined PDFF‐T 1 phantom. Study Type Prospective phantom study. Phantom Commercial PDFF‐T 1 Phantom (Model 725) with varying PDFF (0%–30%) and T 1 (200–1400 ms) values. Field Strength/Sequence 1.5 T and 3 T multi‐echo, three‐dimensional spoiled‐gradient‐echo (SGRE) sequence for PDFF mapping, and MOLLI sequence (5(3)3 acquisition scheme) using two‐dimensional SGRE readouts for T 1 mapping across four centers and vendors. Assessment PDFF and T 1 maps were acquired using standardized protocols. PDFF maps were reconstructed locally, while T 1 maps were generated using a centralized algorithm. All maps were quantitatively analyzed by a single radiologist using standardized region‐of‐interest placement. Phantom temporal stability was assessed at one center across five sessions over 9 months (baseline, retest, 1 week, 6 and 9 months). Statistical Tests Intraclass correlation coefficients (ICC), reproducibility coefficients (RDC), and linear regression analysis were used. A p value < 0.05 was considered statistically significant. Results PDFF showed overall excellent reproducibility (ICC = 0.987, RDC = 3.7%), with increased variability at higher T 1 values (RDC up to 7.9% at T 1 = 1400 ms). T 1 mapping showed good reproducibility in the absence of fat (RDC 16–161 ms at PDFF = 0%), but moderate to poor reproducibility in the presence of fat, with RDC increasing up to 1553 ms at PDFF 30%. Temporal stability was excellent ICC ≥ 0.998 for both PDFF and T 1 , and RDC of 1.1%–1.3% for PDFF and 52–57 ms for T 1 . Data Conclusion This phantom study demonstrated high reproducibility of PDFF, whereas T 1 reproducibility deteriorated at higher fat and T 1 levels, underscoring the need for fat‐corrected T 1 mapping for reliable assessment of fibroinflammatory changes. Evidence Level N/A. Technical Efficacy Stage 1.

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton‐density fat fraction, and cardiacT1. These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic.

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.