Validation of a fast method for quantification of intra-abdominal and subcutaneous adipose tissue for large-scale XXXX studies.

@entity1 @entity1144 @entity74 @entity1386
@entity1144 is the hallmark of a number of @entity74 . 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 @entity1386 ( @entity1386 ) 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 @entity1386 , the inter-observer coefficient of variation was 1.4% for IAAT and 1.2% for @entity1386 , the intra-observer correlation was 0.998 for IAAT and 0.999 for @entity1386 , and the inter-observer correlation was 0.999 for both IAAT and @entity1386 . 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 @entity1 phenotypic studies. Copyright    2015 John Wiley _ Sons, Ltd.
