Literature DB >> 22246813

Comparison of visceral adipose tissue quantification on water suppressed and nonwater-suppressed MRI at 3.0 Tesla.

Anqi Zhou1, Horacio Murillo, Kenneth Cusi, Qi Peng.   

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.
Copyright © 2012 Wiley Periodicals, Inc.

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Year:  2012        PMID: 22246813     DOI: 10.1002/jmri.23582

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  2 in total

1.  Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle.

Authors:  Alexander Valentinitsch; Dimitrios C Karampinos; Hamza Alizai; Karupppasamy Subburaj; Deepak Kumar; Thomas M Link; Sharmila Majumdar
Journal:  J Magn Reson Imaging       Date:  2012-10-23       Impact factor: 4.813

2.  Calibration of a semi-automated segmenting method for quantification of adipose tissue compartments from magnetic resonance images of mice.

Authors:  Philippe Garteiser; Sabrina Doblas; Rheal A Towner; Timothy M Griffin
Journal:  Metabolism       Date:  2013-07-25       Impact factor: 8.694

  2 in total

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