title: Comparison of gross body fat-water magnetic resonance imaging at 3 Tesla to dual-energy X-ray absorptiometry in obese women.
abstract: OBJECTIVE
  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.
  
  
  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/m(2) .
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Division of Gastroenterology, Hepatology, and Nutrition, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. Heidi.j.silver@vanderbilt.edu
authors.forename: Heidi J
authors.initials: HJ
authors.lastname: Silver
authors.forename: Kevin D
authors.initials: KD
authors.lastname: Niswender
authors.forename: Joel
authors.initials: J
authors.lastname: Kullberg
authors.forename: Johan
authors.initials: J
authors.lastname: Berglund
authors.forename: Lars
authors.initials: L
authors.lastname: Johansson
authors.forename: Morten
authors.initials: M
authors.lastname: Bruvold
authors.forename: Malcolm J
authors.initials: MJ
authors.lastname: Avison
authors.forename: E Brian
authors.initials: EB
authors.lastname: Welch
mesh_terms: D015502:Absorptiometry, Photon / Q000379:methods*; D000273:Adipose Tissue / Q000737:chemistry*; D050154:Adiposity; D000328:Adult; D000886:Anthropometry; D015992:Body Mass Index; D005260:Female; D006801:Humans; D050152:Intra-Abdominal Fat / Q000737:chemistry; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D009765:Obesity / Q000378:metabolism*; D055815:Young Adult