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Rodent Abdominal Adipose Tissue Imaging by MR

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Preclinical MRI

Part of the book series: Methods in Molecular Biology ((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.

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References

  1. Brochu M, Poehlman, Ades PA (2000) Obesity, body fat distribution, and coronary artery disease. J Cardpulm Rehabil 20(2):96–108

    Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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):68–75

    Article  CAS  PubMed  Google Scholar 

  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):20–34

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Article  Google Scholar 

  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):194–202

    Article  CAS  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  PubMed Central  Google Scholar 

  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):178–189

    Article  PubMed  PubMed Central  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

  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

    Article  PubMed  Google Scholar 

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Correspondence to Bhanu Prakash KN PhD .

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KN, B., Yaligar, J., Verma, S.K., Gopalan, V., Sendhil Velan, S. (2018). Rodent Abdominal Adipose Tissue Imaging by MR. In: García Martín, M., López Larrubia, P. (eds) Preclinical MRI. Methods in Molecular Biology, vol 1718. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7531-0_15

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  • DOI: https://doi.org/10.1007/978-1-4939-7531-0_15

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7530-3

  • Online ISBN: 978-1-4939-7531-0

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