Reproducibility and repeatability of MRI‐based body composition analysis

Abstract

Purpose There is an absence of reproducibility studies on MRI‐based body composition analysis in current literature. Therefore, the aim of this study was to investigate the between‐scanner reproducibility and the repeatability of a method for MRI‐based body composition analysis. Methods Eighteen healthy volunteers of varying body mass index and adiposity were each scanned twice on five different 1.5T and 3T scanners from three different vendors. Two‐point Dixon neck‐to knee images and two additional liver scans were acquired with similar protocols. Visceral adipose tissue (VAT) volume, abdominal subcutaneous adipose tissue (ASAT) volume, thigh muscle volume, and muscle fat infiltration (MFI) in the thigh muscle were measured. Liver proton density fat fraction (PDFF) was assessed using two different methods, the scanner vendor's 6‐point method and an in‐house 2‐point method. Within‐scanner test‐retest repeatability and between‐scanner reproducibility were calculated using analysis of variance. Results Repeatability coefficients were 13 centiliters (cl) (VAT), 24 cl (ASAT), 17 cl (total thigh muscle volume), 0.53% (MFI), and 1.27‐1.37% for liver PDFF. Reproducibility coefficients were 24 cl (VAT), 42 cl (ASAT), 31 cl (total thigh muscle volume), 1.44% (MFI), and 2.37‐2.40% for liver PDFF. Conclusion For all measures except MFI, the within‐scanner repeatability explained much of the overall reproducibility. The two methods for measuring liver fat had similar reproducibility. This study showed that the investigated method eliminates effects due to scanner differences. The results can be used for power calculations in clinical studies or to better understand the scanner‐induced variability in clinical applications.

Psoriatic arthritis is associated with adverse body composition predictive of greater coronary heart disease and type 2 diabetes propensity – a cross-sectional study

“…While IMAPA imaging was conducted in a different centre to UK Biobank, all images were obtained using standardised AMRA protocols and analysed centrally by AMRA. Further, between MR scanner bias and reproducibility has recently been published and has shown that the magnitude of any systematic differences between MR scanners is smaller than the effect sizes observed in this study, lending confidence that the findings we report are genuine and robust [ 11 ].…”

“…All participants underwent standardized imaging protocols with analyses read in a blinded fashion. Between MR scanner bias and reproducibility coefficients have been recently published by AMRA Medical AB, Linkoping, Sweden [ 11 ]. The propensity for CHD or type 2 diabetes based on body composition was calculated according to the method described by Linge et al [ 12 ].…”

Effects of Sex and Age on Fat Fraction, Diffusion-Weighted Image Signal Intensity and Apparent Diffusion Coefficient in the Bone Marrow of Asymptomatic Individuals: A Cross-Sectional Whole-Body MRI Study

“…The differences may be attributed to our inclusion of skeletal regions with higher fat content compared to lumbar vertebrae (e.g., pelvis), as it has been shown that a large heterogeneity in bone marrow fat fractions exists across anatomical regions [35]. In addition, the 2-point T1-weighted Dixon images used for calculating the relative %FF in our study are sensitive to T1 weighting and possibly T2 decay effects [36], that may lead to measurement bias.…”

Harnessing Muscle–Liver Crosstalk to Treat Nonalcoholic Steatohepatitis

“…Although low muscle volume alone confers greater risk of functional disability in those with NAFLD as compared to age-, gender-, and BMI-matched controls, the current evidence suggests that the presence of both low muscle volume and high muscle fat may amplify this risk ( Figure 3 ). Thus, assessing muscle composition in NAFLD using a non-ionizing radiation technique such as MRI that can reliably and reproducibly assess longitudinal changes in muscle composition over time (test-retest repeatability coefficient was 0.53 percentage points for muscle fat infiltration) ( 73 ), enables its utilization in clinical trial settings to more robustly characterize both pathophysiology and prognosis: the ability to differentiate between vulnerable and normal sub-groups would aid in selecting a more appropriate (and homogenous) NAFLD population for clinical trials, and in tailoring appropriate therapeutic interventions. The availability of objective and highly reliable biomarkers of overall body composition, including muscle quantity and quality ( 73 ), would also enable tracking of muscle health, sarcopenic processes, and comorbidities at a much earlier stage and before onset of physical dysfunction.…”