Why the study?
Can a Gaussian mixture model using water-fat MRI accurately separate and quantify brown and white adipose tissue in adolescents?
Can a Gaussian mixture model using water-fat MRI accurately separate and quantify brown and white adipose tissue in adolescents?
A Gaussian mixture model applied to water-fat MRI can successfully segment and quantify brown and white adipose tissue in adolescents, showing good reproducibility and differences between obese and normal-weight individuals.
GMM-based MRI separation enables BAT quantification in adolescents; leaves open validation for metabolic research applications.
PURPOSE: To develop a technique for the separation and quantification of brown adipose tissue (BAT) and white adipose tissue (WAT) using fat fraction and T2* intensity based on the Gaussian mixture model (GMM). MATERIALS AND BAT and WAT were clustered based on the Gaussian mixture model using the expectation-maximization algorithm. Results and reproducibility were compared and assessed using independent t-tests and intraclass correlation coefficient. RESULTS: BAT in obese participants was predominately found at the supraclavicular region and in normal-weight participants it was more scattered and distributed in interscapular-supraclavicular, axillary, and spine regions. Absolute volume of BAT was higher in the obese group (Obese: 315.2 mL [±89.1], Normal weight: 248.5 mL [±86.4]), but BAT/WAT ratios were significantly higher (P = 0.029) in the normal group. T2* of BAT (P = 0.04) and volume of WAT (P < 0.001) were significantly lower in the normals. Within-group comparison between male and female indicated no significant differences were found in volume (P = 0.776 (normal), 0.501 [obese]), T2* (P = 0.908 [normal], 0.249 [obese]) and fat-fraction of BAT (P = 0.985 [normal], 0.108 [obese]). The intraclass correlation coefficient showed a good reproducibility in volume (BAT: 0.997, WAT: 0.948), T2* (BAT: 0.969, WAT: 0.983), and fat-fraction (BAT: 0.952, WAT: 0.517). CONCLUSION: BAT identified by this method was in agreement with other studies in terms of location, fat-fraction value, and T2* intensity. The proposed GMM-based segmentation could be a useful nonradiation imaging method for assessment of adipose tissue, in particular for serial follow-up of volume changes after drug or lifestyle interventions for obesity. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 1 J. MAGN. RESON. IMAGING 2017;46:758-768.
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Hui et al. (2017) studied this question.
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