Comparison of visceral adipose tissue quantification on water suppressed and nonwater-suppressed MRI at 3.0 Tesla |
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Authors: | Zhou Anqi Murillo Horacio Cusi Kenneth Peng Qi |
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Affiliation: | Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA. |
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Abstract: | Purpose: To systematically evaluate and compare the performance of water‐saturated and nonwater‐saturated T1‐weighted 3.0 T magnetic resonance imaging (MRI) in the application of visceral adipose tissue (VAT) quantification. Materials and Methods: Forty‐five patients underwent abdomen MRI using two different sequences at 3.0 T: 1) a traditional T1‐weighted gradient echo sequence, and 2) the same sequence with water presaturation to enhance fat and nonfat contrast. VAT amounts from both water‐saturated and nonwater‐saturated images were quantified with a manual thresholding technique and an automated segmentation method to study quantification variability and consistency of the two imaging techniques. Results: Nonwater‐saturated MRI had significantly larger coefficient of variation than water‐saturated MRI in the imaging reproducibility study based on 112 slices from seven subjects (11.4% vs. 2.5%, P < 0.0001). VAT volumes measured from the nonwater‐saturation MRI sequence had significantly higher variability than those from water‐saturation images even when using a manual quantification method based on images from 38 subjects (1.76% vs. 1.08%, P < 0.001). In addition, the VAT volume amounts from nonwater‐saturation images and water‐saturated images quantified with the automatic and manual quantification methods were statistically consistent. Conclusion: Water‐saturated MRI sequences at 3.0 T for VAT quantification improve reproducibility and decrease variability compared with nonwater saturated sequences, especially with the use of automatic quantification methods. J. Magn. Reson. Imaging 2012;35:1445–1452. © 2012 Wiley Periodicals, Inc. |
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Keywords: | abdominal MRI obesity visceral adipose tissue body composition image segmentation |
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