## Abstract ## Purpose: To investigate the feasibility of using IDEAL (Iterative Decomposition with Echo Asymmetry and Least squares estimation) fat–water imaging and the resultant fat fraction metric in detecting brown adipose tissue (BAT) in mice, and in differentiating BAT from white adipose ti
Adipose tissue distribution in children: Automated quantification using water and fat MRI
✍ Scribed by Joel Kullberg; Ann-Katrine Karlsson; Eira Stokland; Pär-Arne Svensson; Jovanna Dahlgren
- Publisher
- John Wiley and Sons
- Year
- 2010
- Tongue
- English
- Weight
- 256 KB
- Volume
- 32
- Category
- Article
- ISSN
- 1053-1807
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
Purpose:
To develop and validate a method for rapid acquisition and automated processing of magnetic resonance (MR) images for analysis of abdominal adipose tissue distribution in children.
Materials and Methods:
The study included 21 (10 girls, 11 boys) healthy 5‐year‐old children. Rapid water and fat MR imaging (6 sec) was performed using a 2‐point‐Dixon technique on a 1.5T MR scanner using an 8‐channel cardiac coil. An automated image processing algorithm was developed for automated segmentation of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT), respectively. The results from the fully automated analysis were compared to those from a semiautomated analysis, performed by three operators, from the same images.
Results:
The automated analysis was seen to give results with strong correlation to the reference measurements (r ≥ 0.997); however, the SAT volume was underestimated by 9.4 ± 3.8%. The accuracy of the automated segmentation of VAT and SAT (TP: true positive, FP: false positive, mean ± SD, %) was TP: 83.6 ± 8.5, FP: 12.7 ± 6.8; and TP: 89.9 ± 3.6, FP: 0.7 ± 0.3, respectively.
Conclusion:
A method for rapid imaging and fully automated postprocessing of abdominal adipose tissue distribution is presented. The method allows robust and time‐efficient measurement of adipose tissue distribution in young children. J. Magn. Reson. Imaging 2010. © 2010 Wiley‐Liss, Inc.
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