Canine body composition quantification using 3 tesla fat–water MRI

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.

[1]  A. Cherrington,et al.  Chronic consumption of a high-fat/high-fructose diet renders the liver incapable of net hepatic glucose uptake. , 2010, American journal of physiology. Endocrinology and metabolism.

[2]  H. Minuk,et al.  Metabolic syndrome. , 2005, Journal of insurance medicine.

[3]  E. Merkle,et al.  A review of MR physics: 3T versus 1.5T. , 2007, Magnetic resonance imaging clinics of North America.

[4]  M Alpsten,et al.  A multicompartment body composition technique based on computerized tomography. , 1994, International journal of obesity and related metabolic disorders : journal of the International Association for the Study of Obesity.

[5]  Joel Kullberg,et al.  Model‐based mapping of fat unsaturation and chain length by chemical shift imaging—phantom validation and in vivo feasibility , 2012, Magnetic resonance in medicine.

[6]  R. Bergman,et al.  Novel canine models of obese prediabetes and mild type 2 diabetes. , 2010, American journal of physiology. Endocrinology and metabolism.

[7]  Krishna S Nayak,et al.  Automatic intra‐subject registration‐based segmentation of abdominal fat from water–fat MRI , 2013, Journal of magnetic resonance imaging : JMRI.

[8]  Joakim Lindblad,et al.  Sub-pixel Segmentation with the Image Foresting Transform , 2009, IWCIA.

[9]  Gastric Bypass Promotes More Lipid Mobilization Than a Similar Weight Loss Induced by Low-Calorie Diet , 2011, Journal of obesity.

[10]  M. Okumura,et al.  Computed tomographic assessment of body fat in beagles. , 2005, Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association.

[11]  Anand A. Joshi,et al.  Automatic Intra-Subject Registration-Based Segmentation of Abdominal Fat From Three-Dimensional Water – Fat MRI , 2012 .

[12]  S. Schoenberg,et al.  Artifacts in 3-T MRI: physical background and reduction strategies. , 2008, European journal of radiology.

[13]  Peter Börnert,et al.  Automated assessment of whole‐body adipose tissue depots from continuously moving bed MRI: A feasibility study , 2009, Journal of magnetic resonance imaging : JMRI.

[14]  J. Kullberg,et al.  Comparison of Gross Body Fat-Water Magnetic Resonance Imaging at 3 Tesla to Dual Energy X-Ray Absorptiometry in Obese Women , 2012, Obesity.

[15]  Håkan Ahlström,et al.  Three‐point dixon method enables whole‐body water and fat imaging of obese subjects , 2010, Magnetic resonance in medicine.

[16]  J. Shaw,et al.  IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. , 2011, Diabetes research and clinical practice.

[17]  C. Sirlin,et al.  In vivo characterization of the liver fat 1H MR spectrum , 2011, NMR in biomedicine.

[18]  B. Aldefeld,et al.  Whole‐body 3D water/fat resolved continuously moving table imaging , 2007, Journal of magnetic resonance imaging : JMRI.

[19]  Hong Yan,et al.  Image segmentation based on adaptive cluster prototype estimation , 2005, IEEE Transactions on Fuzzy Systems.