Literature DB >> 23596090

Canine body composition quantification using 3 tesla fat-water MRI.

Aliya Gifford1, Joel Kullberg, Johan Berglund, Filip Malmberg, Katie C Coate, Phillip E Williams, Alan D Cherrington, Malcolm J Avison, E Brian Welch.   

Abstract

PURPOSE: To test the hypothesis that a whole-body fat-water MRI (FWMRI) protocol acquired at 3 Tesla combined with semi-automated image analysis techniques enables precise volume and mass quantification of adipose, lean, and bone tissue depots that agree with static scale mass and scale mass changes in the context of a longitudinal study of large-breed dogs placed on an obesogenic high-fat, high-fructose diet.
MATERIALS AND METHODS: Six healthy adult male dogs were scanned twice, at weeks 0 (baseline) and 4, of the dietary regiment. FWMRI-derived volumes of adipose tissue (total, visceral, and subcutaneous), lean tissue, and cortical bone were quantified using a semi-automated approach. Volumes were converted to masses using published tissue densities.
RESULTS: FWMRI-derived total mass corresponds with scale mass with a concordance correlation coefficient of 0.931 (95% confidence interval = [0.813, 0.975]), and slope and intercept values of 1.12 and -2.23 kg, respectively. Visceral, subcutaneous and total adipose tissue masses increased significantly from weeks 0 to 4, while neither cortical bone nor lean tissue masses changed significantly. This is evidenced by a mean percent change of 70.2% for visceral, 67.0% for subcutaneous, and 67.1% for total adipose tissue.
CONCLUSION: FWMRI can precisely quantify and map body composition with respect to adipose, lean, and bone tissue depots. The described approach provides a valuable tool to examine the role of distinct tissue depots in an established animal model of human metabolic disease.
Copyright © 2013 Wiley Periodicals, Inc.

Entities:  

Keywords:  adipose; bone; canine; fat-water; lean; whole-body

Mesh:

Year:  2013        PMID: 23596090      PMCID: PMC3735857          DOI: 10.1002/jmri.24156

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


  16 in total

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2.  In vivo characterization of the liver fat ¹H MR spectrum.

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Journal:  J Magn Reson Imaging       Date:  2007-03       Impact factor: 4.813

4.  The metabolic syndrome.

Authors:  Robert H Eckel; K G M M Alberti; Scott M Grundy; Paul Z Zimmet
Journal:  Lancet       Date:  2010-01-16       Impact factor: 79.321

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

Authors:  Joel Kullberg; Lars Johansson; Håkan Ahlström; Frederic Courivaud; Peter Koken; Holger Eggers; Peter Börnert
Journal:  J Magn Reson Imaging       Date:  2009-07       Impact factor: 4.813

6.  A multicompartment body composition technique based on computerized tomography.

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7.  Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI.

Authors:  Anand A Joshi; Houchun H Hu; Richard M Leahy; Michael I Goran; Krishna S Nayak
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8.  Chronic consumption of a high-fat/high-fructose diet renders the liver incapable of net hepatic glucose uptake.

Authors:  Katie Colbert Coate; Melanie Scott; Ben Farmer; Mary Courtney Moore; Marta Smith; Joshua Roop; Doss W Neal; Phil Williams; Alan D Cherrington
Journal:  Am J Physiol Endocrinol Metab       Date:  2010-09-07       Impact factor: 4.310

9.  Computed tomographic assessment of body fat in beagles.

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10.  Comparison of gross body fat-water magnetic resonance imaging at 3 Tesla to dual-energy X-ray absorptiometry in obese women.

Authors:  Heidi J Silver; Kevin D Niswender; Joel Kullberg; Johan Berglund; Lars Johansson; Morten Bruvold; Malcolm J Avison; E Brian Welch
Journal:  Obesity (Silver Spring)       Date:  2013-04       Impact factor: 5.002

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  6 in total

Review 1.  Segmentation and quantification of adipose tissue by magnetic resonance imaging.

Authors:  Houchun Harry Hu; Jun Chen; Wei Shen
Journal:  MAGMA       Date:  2015-09-04       Impact factor: 2.310

2.  Fat-water MRI of a diet-induced obesity mouse model at 15.2T.

Authors:  Henry H Ong; Corey D Webb; Marnie L Gruen; Alyssa H Hasty; John C Gore; E Brian Welch
Journal:  J Med Imaging (Bellingham)       Date:  2016-05-24

Review 3.  Animal models of obesity and diabetes mellitus.

Authors:  Maximilian Kleinert; Christoffer Clemmensen; Susanna M Hofmann; Mary C Moore; Simone Renner; Stephen C Woods; Peter Huypens; Johannes Beckers; Martin Hrabe de Angelis; Annette Schürmann; Mostafa Bakhti; Martin Klingenspor; Mark Heiman; Alan D Cherrington; Michael Ristow; Heiko Lickert; Eckhard Wolf; Peter J Havel; Timo D Müller; Matthias H Tschöp
Journal:  Nat Rev Endocrinol       Date:  2018-01-19       Impact factor: 43.330

4.  In vivo assessment of subcutaneous fat in dogs by real-time ultrasonography and image analysis.

Authors:  Rita Payan-Carreira; Luis Martins; Sónia Miranda; Pedro Olivério; Severiano R Silva
Journal:  Acta Vet Scand       Date:  2016-10-20       Impact factor: 1.695

5.  Sympathetic Denervation of the Common Hepatic Artery Lessens Glucose Intolerance in the Fat- and Fructose-Fed Dog.

Authors:  Guillaume Kraft; Anthony Vrba; Melanie Scott; Eric Allen; Dale S Edgerton; Phil E Williams; Scott B Vafai; Bobak R Azamian; Alan D Cherrington
Journal:  Diabetes       Date:  2019-04-01       Impact factor: 9.461

6.  Evaluation of visceral fat mass in dogs by computed tomography.

Authors:  Itsuma Nagao; Koichi Ohno; Takuro Nagahara; Nozomu Yokoyama; Taisuke Nakagawa; Reina Fujiwara; Kie Yamamoto; Yuko Goto-Koshino; Hirotaka Tomiyasu; Hajime Tsujimoto
Journal:  J Vet Med Sci       Date:  2019-09-25       Impact factor: 1.267

  6 in total

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