Rodent Abdominal Adipose Tissue Imaging by MR

  • Bhanu Prakash KN
  • Jadegoud Yaligar
  • Sanjay K. Verma
  • Venkatesh Gopalan
  • S. Sendhil Velan
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1718)

Abstract

Rodents including rats and mice are important models to study obesity, diabetes, and metabolic syndrome in a preclinical setting. Translational and longitudinal imaging of these rodents permit investigation of metabolic diseases and identification of imaging biomarkers suitable for clinical translation. Here we describe the imaging protocols for achieving quantitative abdominal imaging in small animals followed by segmentation and quantification of fat volumes.

Key words

Magnetic Resonance Imaging Abdomen Rats Mice Segmentation Visceral fat Subcutaneous fat Obesity Quantification 

References

  1. 1.
    Brochu M, Poehlman, Ades PA (2000) Obesity, body fat distribution, and coronary artery disease. J Cardpulm Rehabil 20(2):96–108Google Scholar
  2. 2.
    Colberg SR, Simoneau JA, Thaete FL, Kelley DE (1995) Skeletal muscle utilization of free fatty acids in women with visceral obesity. J Clin Invest 95(4):1846–1853. https://doi.org/10.1172/JCI117864 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Evans DJ, Hoffmann RG, Kalkhoff RK, Kissebah AH (1984) Relationship of body fat topography to insulin sensitivity and metabolic profiles in premenopausal women. Metab Clin Exp 33(1):68–75CrossRefPubMedGoogle Scholar
  4. 4.
    Vague J (1956) The degree of masculine differentiation of obesities: a factor determining predisposition to diabetes, atherosclerosis, gout, and uric calculous disease. Am J Clin Nutr 4(1):20–34CrossRefPubMedGoogle Scholar
  5. 5.
    Kn BP, Gopalan V, Lee SS, Velan SS (2014) Quantification of abdominal fat depots in rats and mice during obesity and weight loss interventions. PLoS One 9(10):e108979. https://doi.org/10.1371/journal.pone.0108979 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Gopalan V, Michael N, Ishino S, Lee SS, Yang AY, Bhanu Prakash KN, Yaligar J, Sadananthan SA, Kaneko M, Zhou Z, Satomi Y, Hirayama M, Kamiguchi H, Zhu B, Horiguchi T, Nishimoto T, Velan SS (2016) Effect of exercise and calorie restriction on tissue acylcarnitines, tissue desaturase indices, and fat accumulation in diet-induced obese rats. Sci Rep 6:26445. https://doi.org/10.1038/srep26445 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Sadananthan SA, Prakash B, Leow MK, Khoo CM, Chou H, Venkataraman K, Khoo EY, Lee YS, Gluckman PD, Tai ES, Velan SS (2015) Automated segmentation of visceral and subcutaneous (deep and superficial) adipose tissues in normal and overweight men. J Magn Reson Imaging 41(4):924–934. https://doi.org/10.1002/jmri.24655 CrossRefPubMedGoogle Scholar
  8. 8.
    Marzola P, Boschi F, Moneta F, Sbarbati A, Zancanaro C (2016) Preclinical in vivo imaging for fat tissue identification, quantification, and functional characterization. Front Pharmacol 7:336. https://doi.org/10.3389/fphar.2016.00336 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    HH H, Kan HE (2013) Quantitative proton MR techniques for measuring fat. NMR Biomed 26(12):1609–1629. https://doi.org/10.1002/nbm.3025 CrossRefGoogle Scholar
  10. 10.
    Schick F (1998) Simultaneous highly selective MR water and fat imaging using a simple new type of spectral-spatial excitation. Magn Reson Med 40(2):194–202CrossRefPubMedGoogle Scholar
  11. 11.
    Reeder SB, McKenzie CA, Pineda AR, Yu H, Shimakawa A, Brau AC, Hargreaves BA, Gold GE, Brittain JH (2007) Water-fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging 25(3):644–652. https://doi.org/10.1002/jmri.20831 CrossRefPubMedGoogle Scholar
  12. 12.
    Pernona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639. https://doi.org/10.1109/34.56205 CrossRefGoogle Scholar
  13. 13.
    Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199. https://doi.org/10.1109/42.996338 CrossRefPubMedGoogle Scholar
  14. 14.
    Peli T, Malah D (1982) A study of edge detection algorithms. Comput Graph Image Process 20(1):1–21. https://doi.org/10.1016/0146-664X(82)90070-3 CrossRefGoogle Scholar
  15. 15.
    Lankton S, Tannenbaum A (2008) Localizing region-based active contours. IEEE Trans Image Process 17(11):2029–2039. https://doi.org/10.1109/TIP.2008.2004611 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Zou KH, Warfield SK, Bharatha A, Tempany CM, Kaus MR, Haker SJ, Wells WM 3rd, Jolesz FA, Kikinis R (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11(2):178–189CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    HH H, Bornert P, Hernando D, Kellman P, Ma J, Reeder S, Sirlin C (2012) ISMRM workshop on fat-water separation: insights, applications and progress in MRI. Magn Reson Med 68(2):378–388. https://doi.org/10.1002/mrm.24369 CrossRefGoogle Scholar
  18. 18.
    Addeman BT, Kutty S, Perkins TG, Soliman AS, Wiens CN, McCurdy CM, Beaton MD, Hegele RA, McKenzie CA (2015) Validation of volumetric and single-slice MRI adipose analysis using a novel fully automated segmentation method. J Magn Reson Imaging 41(1):233–241. https://doi.org/10.1002/jmri.24526 CrossRefPubMedGoogle Scholar
  19. 19.
    Joshi AA, HH H, Leahy RM, Goran MI, Nayak KS (2013) Automatic intra-subject registration-based segmentation of abdominal fat from water-fat MRI. J Magn Reson Imaging 37(2):423–430. https://doi.org/10.1002/jmri.23813 CrossRefPubMedGoogle Scholar
  20. 20.
    Ranefall P, Bidar AW, Hockings PD (2009) Automatic segmentation of intra-abdominal and subcutaneous adipose tissue in 3D whole mouse MRI. J Magn Reson Imaging 30(3):554–560. https://doi.org/10.1002/jmri.21874 CrossRefPubMedGoogle Scholar
  21. 21.
    Shen J, Baum T, Cordes C, Ott B, Skurk T, Kooijman H, Rummeny EJ, Hauner H, Menze BH, Karampinos DC (2016) Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: application to weight-loss in obesity. Eur J Radiol 85(9):1613–1621. https://doi.org/10.1016/j.ejrad.2016.06.006 CrossRefPubMedGoogle Scholar
  22. 22.
    Tang Y, Sharma P, Nelson MD, Simerly R, Moats RA (2011) Automatic abdominal fat assessment in obese mice using a segmental shape model. J Magn Reson Imaging 34(4):866–873. https://doi.org/10.1002/jmri.22690 CrossRefPubMedGoogle Scholar
  23. 23.
    Wald D, Teucher B, Dinkel J, Kaaks R, Delorme S, Boeing H, Seidensaal K, Meinzer HP, Heimann T (2012) Automatic quantification of subcutaneous and visceral adipose tissue from whole-body magnetic resonance images suitable for large cohort studies. J Magn Reson Imaging 36(6):1421–1434. https://doi.org/10.1002/jmri.23775 CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  • Bhanu Prakash KN
    • 1
  • Jadegoud Yaligar
    • 1
  • Sanjay K. Verma
    • 1
  • Venkatesh Gopalan
    • 1
  • S. Sendhil Velan
    • 2
  1. 1.Signal and Image Processing, Singapore Bioimaging Consortium, Agency for Science, Technology and ResearchBiopolis WaySingapore
  2. 2.Metabolic Imaging Group, Singapore Bioimaging Consortium, Agency for Science, Technology and ResearchBiopolis WaySingapore

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