Automated assessment of whole-body adipose tissue depots from continuously moving bed MRI: A feasibility study

Abstract

Purpose: To present an automated algorithm for segmentation of visceral, subcutaneous, and total volumes of adipose tissue depots (VAT, SAT, TAT) from whole-body MRI data sets and to investigate the VAT segmentation accuracy and the reproducibility of all depot assessments. Materials and Methods: Repeated measurements were performed on 24 volunteer subjects using a 1.5 Tesla clinical MRI scanner and a three-dimensional (3D) multigradient-echo sequence (resolution: 2.1 x 2.1 x 8 mm3, acquisition time: 5 min 15 s). Fat and water images were reconstructed, and fully automated segmentation was performed. Manual segmentation of the VAT reference was performed by an experienced operator. Results: Strong correlation (R = 0.999) was found between the automated and manual VAT assessments. The automated results underestimated VAT with 4.7 ± 4.4%. The accuracy was 88 ± 4.5% and 7.6 ± 5.7% for true positive and false positive fractions, respectively. Coefficients of variation from the repeated measurements were: 2.32 % ± 2.61%, 2.25% ± 2.10%, and 1.01% ± 0.74% for VAT, SAT, and TAT, respectively. Conclusion: Automated and manual VAT results correlated strongly. The assessments of all depots were highly reproducible. The acquisition and postprocessing techniques presented are likely useful in obesity related studies. © 2009 Wiley-Liss, Inc.