title: Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale human studies.
abstract: Central obesity is the hallmark of a number of non-inheritable disorders. The advent of imaging techniques such as MRI has allowed for a fast and accurate assessment of body fat content and distribution. However, image analysis continues to be one of the major obstacles to the use of MRI in large-scale studies. In this study we assess the validity of the recently proposed fat-muscle quantitation system (AMRA(TM) Profiler) for the quantification of intra-abdominal adipose tissue (IAAT) and abdominal subcutaneous adipose tissue (ASAT) from abdominal MR images. Abdominal MR images were acquired from 23 volunteers with a broad range of BMIs and analysed using sliceOmatic, the current gold-standard, and the AMRA(TM) Profiler based on a non-rigid image registration of a library of segmented atlases. The results show that there was a highly significant correlation between the fat volumes generated by the two analysis methods, (Pearson correlation r = 0.97, p < 0.001), with the AMRA(TM) Profiler analysis being significantly faster (~3 min) than the conventional sliceOmatic approach (~40 min). There was also excellent agreement between the methods for the quantification of IAAT (AMRA 4.73 ± 1.99 versus sliceOmatic 4.73 ± 1.75 l, p = 0.97). For the AMRA(TM) Profiler analysis, the intra-observer coefficient of variation was 1.6% for IAAT and 1.1% for ASAT, the inter-observer coefficient of variation was 1.4% for IAAT and 1.2% for ASAT, the intra-observer correlation was 0.998 for IAAT and 0.999 for ASAT, and the inter-observer correlation was 0.999 for both IAAT and ASAT. These results indicate that precise and accurate measures of body fat content and distribution can be obtained in a fast and reliable form by the AMRA(TM) Profiler, opening up the possibility of large-scale human phenotypic studies.
authors.affiliation: Department of Biomedical Engineering, Linköping University, Sweden.|Centre for Medical Image Science and Visualization (CMIV), Linköping University, Sweden.|Advanced MR Analytics AB, Linköping, Sweden.
authors.forename: Magnus
authors.initials: M
authors.lastname: Borga
authors.affiliation: Department of Life Sciences, Faculty of Science and Technology, University of Westminster, London, UK.
authors.forename: E Louise
authors.initials: EL
authors.lastname: Thomas
authors.affiliation: Department of Biomedical Engineering, Linköping University, Sweden.|Centre for Medical Image Science and Visualization (CMIV), Linköping University, Sweden.
authors.forename: Thobias
authors.initials: T
authors.lastname: Romu
authors.affiliation: Advanced MR Analytics AB, Linköping, Sweden.
authors.forename: Johannes
authors.initials: J
authors.lastname: Rosander
authors.affiliation: Department of Life Sciences, Faculty of Science and Technology, University of Westminster, London, UK.
authors.forename: Julie
authors.initials: J
authors.lastname: Fitzpatrick
authors.affiliation: Advanced MR Analytics AB, Linköping, Sweden.|Department of Medical and Health Sciences, Linköping University, Sweden.
authors.forename: Olof
authors.initials: O
authors.lastname: Dahlqvist Leinhard
authors.affiliation: Department of Life Sciences, Faculty of Science and Technology, University of Westminster, London, UK.
authors.forename: Jimmy D
authors.initials: JD
authors.lastname: Bell
mesh_terms: D050153:Abdominal Fat / Q000473:pathology*; D050154:Adiposity; D000328:Adult; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D050507:Subcutaneous Fat, Abdominal / Q000473:pathology*
keywords: Dixon; MRI; abdominal fat; adipose tissue; fat quantitation; obesity