Disclosure: L.J. is partly employed by AstraZeneca. M.B. is employed by Philips Healthcare. The other authors have no duality of interest to disclose.
Article first published online: 25 MAY 2013
Copyright © 2012 The Obesity Society
Volume 21, Issue 4, pages 765–774, April 2013
How to Cite
Silver, H. J., Niswender, K. D., Kullberg, J., Berglund, J., Johansson, L., Bruvold, M., Avison, M. J. and Welch, E.Brian. (2013), Comparison of gross body fat-water magnetic resonance imaging at 3 Tesla to dual-energy X-ray absorptiometry in obese women. Obesity, 21: 765–774. doi: 10.1002/oby.20287
See the online ICMJE Conflict of Interest Forms for this article.
- Issue published online: 25 MAY 2013
- Article first published online: 25 MAY 2013
- Manuscript Accepted: 13 MAY 2012
- Manuscript Received: 29 JUN 2011
Improved understanding of how depot-specific adipose tissue mass predisposes to obesity-related comorbidities could yield new insights into the pathogenesis and treatment of obesity as well as metabolic benefits of weight loss. We hypothesized that three-dimensional (3D) contiguous “fat-water” MR imaging (FWMRI) covering the majority of a whole-body field of view (FOV) acquired at 3 Tesla (3T) and coupled with automated segmentation and quantification of amount, type, and distribution of adipose and lean soft tissue would show great promise in body composition methodology.
Design and Methods:
Precision of adipose and lean soft tissue measurements in body and trunk regions were assessed for 3T FWMRI and compared to dual-energy X-ray absorptiometry (DXA). Anthropometric, FWMRI, and DXA measurements were obtained in 12 women with BMI 30-39.9 kg/m2.
Test–retest results found coefficients of variation (CV) for FWMRI that were all under 3%: gross body adipose tissue (GBAT) 0.80%, total trunk adipose tissue (TTAT) 2.08%, visceral adipose tissue (VAT) 2.62%, subcutaneous adipose tissue (SAT) 2.11%, gross body lean soft tissue (GBLST) 0.60%, and total trunk lean soft tissue (TTLST) 2.43%. Concordance correlation coefficients between FWMRI and DXA were 0.978, 0.802, 0.629, and 0.400 for GBAT, TTAT, GBLST, and TTLST, respectively.
While Bland–Altman plots demonstrated agreement between FWMRI and DXA for GBAT and TTAT, a negative bias existed for GBLST and TTLST measurements. Differences may be explained by the FWMRI FOV length and potential for DXA to overestimate lean soft tissue. While more development is necessary, the described 3T FWMRI method combined with fully-automated segmentation is fast (<30-min total scan and post-processing time), noninvasive, repeatable, and cost-effective.