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

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.

Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI.

PURPOSE: To develop and demonstrate a rapid whole-body magnetic resonance imaging (MRI) method for automatic quantification of total and regional skeletal muscle volume. MATERIALS AND METHODS: The method was based on a multi-atlas segmentation of intensity corrected water-fat separated image volumes. Automatic lean muscle tissue segmentations were achieved by nonrigid registration of atlas datasets with 10 different manually segmented muscle groups. Ten subjects scanned at 1.5 T and 3.0 T were used as atlases, initial validation and optimization. Further validation used 11 subjects scanned at 3.0 T. The automated and manual segmentations were compared using intraclass correlation, true positive volume fractions, and delta volumes. RESULTS: For the 1.5 T datasets, the intraclass correlation, true positive volume fractions (mean ± standard deviation, SD), and delta volumes (mean ± SD) were 0.99, 0.91 ± 0.02, -0.10 ± 0.70L (whole body), 0.99, 0.93 ± 0.02, 0.01 ± 0.07L (left anterior thigh), and 0.98, 0.80 ± 0.07, -0.08 ± 0.15L (left abdomen). The corresponding values at 3.0 T were 0.97, 0.92 ± 0.03, -0.17 ± 1.37L (whole body), 0.99, 0.93 ± 0.03, 0.03 ± 0.08L (left anterior thigh), and 0.89, 0.90 ± 0.04, -0.03 ± 0.42L (left abdomen). The validation datasets showed similar results. CONCLUSION: The method accurately quantified the whole-body skeletal muscle volume and the volume of separate muscle groups independent of field strength and image resolution.

Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system.

PURPOSE: To measure the test-retest reproducibility of an automated system for quantifying whole body and compartmental muscle volumes using wide bore 3 T MRI. MATERIALS AND METHODS: Thirty volunteers stratified by body mass index underwent whole body 3 T MRI, two-point Dixon sequences, on two separate occasions. Water-fat separation was performed, with automated segmentation of whole body, torso, upper and lower leg volumes, and manually segmented lower leg muscle volumes. RESULTS: Mean automated total body muscle volume was 19·32 L (SD9·1) and 19·28 L (SD9·12) for first and second acquisitions (Intraclass correlation coefficient (ICC) = 1·0, 95% level of agreement -0·32-0·2 L). ICC for all automated test-retest muscle volumes were almost perfect (0·99-1·0) with 95% levels of agreement 1.8-6.6% of mean volume. Automated muscle volume measurements correlate closely with manual quantification (right lower leg: manual 1·68 L (2SD0·6) compared to automated 1·64 L (2SD 0·6), left lower leg: manual 1·69 L (2SD 0·64) compared to automated 1·63 L (SD0·61), correlation coefficients for automated and manual segmentation were 0·94-0·96). CONCLUSION: Fully automated whole body and compartmental muscle volume quantification can be achieved rapidly on a 3 T wide bore system with very low margins of error, excellent test-retest reliability and excellent correlation to manual segmentation in the lower leg. KEY POINTS: Sarcopaenia is an important reversible complication of a number of diseases. Manual quantification of muscle volume is time-consuming and expensive. Muscles can be imaged using in and out of phase MRI. Automated atlas-based segmentation can identify muscle groups. Automated muscle volume segmentation is reproducible and can replace manual measurements.