Skip to main content

Abstract

The most prominent health problem facing the global population is from a combined impact of obesity, diabetes, and cardiovascular disease, where health consequences can include elevated risk for atherosclerosis, dyslipidaemia, insulin resistance, hypertension, liver disease, gallbladder disease, musculoskeletal disorders, and several types of cancer. Continued research is establishing relationships among body composition, ethnicity, genotype, and cardiometabolic risk. Body composition assessment is an interdisciplinary field used for normalization and interpretation of metabolic data, as well as the assessment of nutritional intervention outcome or metabolic risk in overweight and underweight subjects. In order to further these investigations, clinicians and scientists require robust methods to characterize body composition and assess the amount, type (subcutaneous or visceral), and distribution of adipose tissue. Imaging methods are considered by many to be among the most accurate tools available for measuring the different body tissues and organs in clinical research as these methods can provide in depth information about the spatial distribution of the tissues. The focus of this chapter will be on the array of biomedical imaging approaches available for assessing body composition, specifically dual-energy X-ray absorptiometry (DXA), computed tomography (CT), and magnetic resonance imaging (MRI).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Wajchenberg BL. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev. 2000;21(6):697–738.

    Article  CAS  PubMed  Google Scholar 

  2. Bermudez OI, Tucker KL. Total and central obesity among elderly Hispanics and the association with type 2 diabetes. Obesity (Silver Spring). 2001;9(8):443–51.

    Article  CAS  Google Scholar 

  3. Shen W, Wang Z, Punyanita M, Lei J, Sinav A, Kral JG, et al. Adipose tissue quantification by imaging methods: a proposed classification. Obes Res. 2003;11(1):5–16.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Després J-P. The insulin resistance-dyslipidemic syndrome of visceral obesity: effect on patients’ risk. Obes Res. 1998;6(S1):8S–17.

    Article  PubMed  Google Scholar 

  5. Kelley DE, Williams KV, Price JC, McKolanis TM, Goodpaster BH, Thaete FL. Plasma fatty acids, adiposity, and variance of skeletal muscle insulin resistance in type 2 diabetes mellitus. J Clin Endocrinol Metab. 2001;86(11):5412–9.

    Article  CAS  PubMed  Google Scholar 

  6. Kelley DE, Thaete FL, Troost F, Huwe T, Goodpaster BH. Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance. Am J Physiol Endocrinol Metab. 2000;278(5):E941–8.

    CAS  PubMed  Google Scholar 

  7. Goodpaster BH, He J, Watkins S, Kelley DE. Skeletal muscle lipid content and insulin resistance: evidence for a paradox in endurance-trained athletes. J Clin Endocrinol Metab. 2001;86(12):5755–61.

    Article  CAS  PubMed  Google Scholar 

  8. Goodpaster BH, Thaete FL, Kelley DE. Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus. Am J Clin Nutr. 2000;71:885–92.

    CAS  PubMed  Google Scholar 

  9. Goodpaster BH, Krishnaswami S, Resnick H, Kelley DE, Haggerty C, Harris TB, et al. Association between regional adipose tissue distribution and both type 2 diabetes and impaired glucose tolerance in elderly men and women. Diabetes Care. 2003;26(2):372–9.

    Article  PubMed  Google Scholar 

  10. Moleschott J. Physiologie der Nahrungsmittel: Ein Handbuch der Diätetik, Ferber, Giessen, 2nd Edition, 1859, p 224 (https://archive.org/details/physiologiedern00molegoog).

  11. Bischoff E. Einzelne Gewichts- und Trocken-Bestimmungen der Organe des menschlichen Korpers. Fresenius, Zeitschrift f anal Chemie. 1864;3(1):250–4.

    Article  Google Scholar 

  12. Camerer W Jr, Sölder, Camerer Jr. Die chemische Zusammensetzung des Neugeborenen – Google Scholar. Z Biol. 1900;39:173–92.

    Google Scholar 

  13. Mitchell HH, Hamilton TS, Steggerda FR, Bean HW. The chemical composition of the adult human body and its bearing on the biochemistry of growth. J Biol Chem. 1945;158:625–37.

    CAS  Google Scholar 

  14. Widdowson EM, McCance RA, Spray CM. The chemical composition of the human body. Clin Sci. 1951;10(1):113–25.

    CAS  PubMed  Google Scholar 

  15. Behnke AR, Feen BG, Welham WC. The specific gravity of healthy men. JAMA. 1942;118(7):495–8.

    Article  Google Scholar 

  16. Siri WE. Techniques for measuring body composition: proceedings of a conference, quartermaster research and engineering center, Natick, 22–23 Jan 1959. National Academies, 1961.

    Google Scholar 

  17. Thomasset A. Bio-electric properties of tissues. Estimation by measurement of impedance of extracellular ionic strength and intracellular ionic strength in the clinic. Lyon Med. 1963;209:1325–50.

    CAS  PubMed  Google Scholar 

  18. Mazess RB, Cameron JR, Sorenson JA. Determining body composition by radiation absorption spectrometry. Nature. 1970;228(5273):771–2.

    Article  CAS  PubMed  Google Scholar 

  19. Heymsfield SB, Olafson RP, Kutner MH, Nixon DW. A radiographic method of quantifying protein-calorie undernutrition. Am J Clin Nutr. 1979;32:693–702.

    CAS  PubMed  Google Scholar 

  20. Borkan GA, Hults DE, Gerzof SG, Burrows BA, Robbins AH. Relationships between computed tomography tissue areas, thicknesses and total body composition. Ann Hum Biol. 1983;10(6):537–45.

    Article  CAS  PubMed  Google Scholar 

  21. Tokunaga K, Matsuzawa Y, Ishikawa K, Tarui S. A novel technique for the determination of body fat by computed tomography. Int J Obes (Lond). 1983;7(5):437–45.

    CAS  Google Scholar 

  22. Foster MA, Hutchison JMS, Mallard JR, Fuller M. Nuclear magnetic resonance pulse sequence and discrimination of high- and low-fat tissues. Magn Reson Imaging. 1984;2(3):187–92.

    Article  CAS  PubMed  Google Scholar 

  23. Sjöström L, Kvist H, Cederblad A, Tylén U. Determination of total adipose tissue and body fat in women by computed tomography, 40K, and tritium. Am J Physiol. 1986;250(6 Pt 1):E736–45.

    PubMed  Google Scholar 

  24. Tataranni PA, Ravussin E. Use of dual-energy X-ray absorptiometry in obese individuals. Am J Clin Nutr. 1995;62(4):730–4.

    CAS  PubMed  Google Scholar 

  25. Houtkooper LB, Going SB, Sproul J, Blew RM, Lohman TG. Comparison of methods for assessing body-composition changes over 1 y in postmenopausal women. Am J Clin Nutr. 2000;72(2):401–6.

    CAS  PubMed  Google Scholar 

  26. Russell-Aulet M, Wang J, Thornton J, Pierson RN. Comparison of dual-photon absorptiometry systems for total-body bone and soft tissue measurements: dual-energy X-rays versus gadolinium 153. J Bone Miner Res. 1991;6(4):411–5.

    Article  CAS  PubMed  Google Scholar 

  27. Shepherd JA, Fan B, Lu Y, Wu XP, Wacker WK, Ergun DL, et al. A multinational study to develop universal standardization of whole-body bone density and composition using GE Healthcare Lunar and Hologic DXA systems. J Bone Miner Res. 2012;27(10):2208–16.

    Article  PubMed  Google Scholar 

  28. Lee K, Lee S, Kim Y-J, Kim Y-J. Waist circumference, dual-energy X-ray absortiometrically measured abdominal adiposity, and computed tomographically derived intra-abdominal fat area on detecting metabolic risk factors in obese women. Nutrition. 2008;24(7–8):625–31.

    Article  PubMed  Google Scholar 

  29. Wiklund P, Toss F, Weinehall L, Hallmans G, Franks PW, Nordström A, et al. Abdominal and gynoid fat mass are associated with cardiovascular risk factors in men and women. J Clin Endocrinol Metab. 2008;93(11):4360–6.

    Article  CAS  PubMed  Google Scholar 

  30. Micklesfield LK, Goedecke JH, Punyanitya M, Wilson KE, Kelly TL. Dual-energy X-ray performs as well as clinical computed tomography for the measurement of visceral fat. Obesity (Silver Spring). 2012;20(5):1109–14.

    Article  Google Scholar 

  31. Kaul S, Rothney MP, Peters DM, Wacker WK, Davis CE, Shapiro MD, et al. Dual-energy X-ray absorptiometry for quantification of visceral fat. Obesity (Silver Spring). 2012;20(6):1313–8.

    Article  Google Scholar 

  32. Heymsfield S. Human body composition. 2nd ed. Champaign: Human Kinetics; 2005. 1 p.

    Google Scholar 

  33. Sjöström L. A computer-tomography based multicompartment body composition technique and anthropometric predictions of lean body mass, total and subcutaneous adipose tissue. Int J Obes (Lond). 1991;15 Suppl 2:19–30.

    Google Scholar 

  34. Gallagher D, Belmonte D, Deurenberg P, Wang Z, Krasnow N, Pi Sunyer FX, et al. Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass. Am J Physiol. 1998;275(2 Pt 1):E249–58.

    CAS  PubMed  Google Scholar 

  35. Kelley DE, McKolanis TM, Hegazi RAF, Kuller LH, Kalhan SC. Fatty liver in type 2 diabetes mellitus: relation to regional adiposity, fatty acids, and insulin resistance. Am J Physiol Endocrinol Metab Am Physiol Soc. 2003;285(4):E90–16.

    Google Scholar 

  36. Kvist H, Sjöström L, Tylén U. Adipose tissue volume determinations in women by computed tomography: technical considerations. Int J Obes (Lond). 1986;10(1):53–67.

    CAS  Google Scholar 

  37. Fujioka S, Matsuzawa Y, Tokunaga K, Tarui S. Contribution of intra-abdominal fat accumulation to the impairment of glucose and lipid metabolism in human obesity. Metab Clin Exp. 1987;36(1):54–9.

    Article  CAS  PubMed  Google Scholar 

  38. Mårin P, Andersson B, Ottosson M, Olbe L, Chowdhury B, Kvist H, et al. The morphology and metabolism of intraabdominal adipose tissue in men. Metab Clin Exp. 1992;41(11):1242–8.

    Article  PubMed  Google Scholar 

  39. Goodpaster BH. Measuring body fat distribution and content in humans. Curr Opin Clin Nutr Metab Care. 2002;5(5):481–7.

    Article  PubMed  Google Scholar 

  40. Deans HE, Smith FW, Lloyd DJ, Law AN, Sutherland HW. Fetal fat measurement by magnetic resonance imaging. Br J Radiol. 1989;62(739):603–7.

    Article  CAS  PubMed  Google Scholar 

  41. Shen W, Wang Z, Tang H, Heshka S, Punyanitya M, Zhu S, et al. Volume estimates by imaging methods: model comparisons with visible woman as the reference. Obes Res. 2003;11(2):217–25.

    Article  PubMed Central  PubMed  Google Scholar 

  42. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge M-P, Albu J, et al. Visceral adipose tissue: relations between single-slice areas and total volume. Am J Clin Nutr. 2004;80(2):271–8.

    CAS  PubMed Central  PubMed  Google Scholar 

  43. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge M-P, Albu J, et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol. 2004;97(6):2333–8.

    Article  PubMed  Google Scholar 

  44. Szczepaniak LS, Babcock EE, Schick F, Dobbins RL, Garg A, Burns DK, et al. Measurement of intracellular triglyceride stores by H spectroscopy: validation in vivo. Am J Physiol. 1999;276(5 Pt 1):E977–89.

    CAS  PubMed  Google Scholar 

  45. Boesch C, Slotboom J, Hoppeler H, Kreis R. In vivo determination of intra-myocellular lipids in human muscle by means of localized 1H-MR-spectroscopy. Magn Reson Med. 1997;37(4):484–93.

    Article  CAS  PubMed  Google Scholar 

  46. Jacob S, Machann J, Rett K, Brechtel K, Volk A, Renn W, et al. Association of increased intramyocellular lipid content with insulin resistance in lean nondiabetic offspring of type 2 diabetic subjects. Diabetes. 1999;48(5):1113–9.

    Article  CAS  PubMed  Google Scholar 

  47. Petersen KF, West AB, Reuben A, Rothman DL, Shulman GI. Noninvasive assessment of hepatic triglyceride content in humans with 13C nuclear magnetic resonance spectroscopy. Hepatology. 1996;24(1):114–7.

    CAS  PubMed  Google Scholar 

  48. Cheng H-LM, Stikov N, Ghugre NR, Wright GA. Practical medical applications of quantitative MR relaxometry. J Magn Reson Imaging. 2012;36(4):805–24.

    Article  PubMed  Google Scholar 

  49. MacKay A, Whittall K, Adler J, Li D, Paty D, Graeb D. In vivo visualization of myelin water in brain by magnetic resonance. Magn Reson Med. 1994;31(6):673–7.

    Article  CAS  PubMed  Google Scholar 

  50. Chebrolu VV, Hines CDG, Yu H, Pineda AR, Shimakawa A, McKenzie CA, et al. Independent estimation of T*2 for water and fat for improved accuracy of fat quantification. Magn Reson Med. 2010;63(4):849–57.

    Article  PubMed Central  PubMed  Google Scholar 

  51. Clark PR, Chua-anusorn W, St Pierre TG. Bi-exponential proton transverse relaxation rate (R2) image analysis using RF field intensity-weighted spin density projection: potential for R2 measurement of iron-loaded liver. Magn Reson Imaging. 2003;21(5):519–30.

    Article  PubMed  Google Scholar 

  52. Huang C, Graff C, Bilgin A, Altbach MI. Fast MR Parameter Mapping from Highly Undersampled Data by Direct Reconstruction of Principal Component Coefficient Maps Using Compressed Sensing. ISMRM Stockholm; 2010.

    Google Scholar 

  53. Clark PR, St Pierre TG. Quantitative mapping of transverse relaxivity (1/T(2)) in hepatic iron overload: a single spin-echo imaging methodology. Magn Reson Imaging. 2000;18(4):431–8.

    Article  CAS  PubMed  Google Scholar 

  54. Dixon WT. Simple proton spectroscopic imaging. Radiology. 1984;153(1):189–94.

    Article  CAS  PubMed  Google Scholar 

  55. Reeder SB, Wen Z, Yu H, Pineda AR, Gold GE, Markl M, et al. Multicoil Dixon chemical species separation with an iterative least-squares estimation method. Magn Reson Med. 2004;51(1):35–45.

    Article  CAS  PubMed  Google Scholar 

  56. Berglund J, Johansson L, Ahlström H, Kullberg J. Three-point Dixon method enables whole-body water and fat imaging of obese subjects. Magn Reson Med. 2010;63(6):1659–68.

    Article  PubMed  Google Scholar 

  57. Reeder SB, Robson PM, Yu H, Shimakawa A, Hines CDG, McKenzie CA, et al. Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling. J Magn Reson Imaging. 2009;29(6):1332–9.

    Article  PubMed Central  PubMed  Google Scholar 

  58. Hines CDG, Yokoo T, Bydder M, Sirlin CB, Reeder SB. Optimization of flip angle to allow tradeoffs in T1 bias and SNR performance for fat quantification. Proc Intl Soc Mag Reson Med. 18th Annual Meeting, Stockholm; 2010.

    Google Scholar 

  59. Hu HH, Yin L, Aggabao PC, Perkins TG, Chia JM, Gilsanz V. Comparison of brown and white adipose tissues in infants and children with chemical-shift-encoded water-fat MRI. J Magn Reson Imaging. 2013;38(4):885–96.

    Article  PubMed Central  PubMed  Google Scholar 

  60. Ma J, Costelloe CM, Madewell JE, Hortobagyi GN, Green MC, Cao G, et al. Fast dixon-based multisequence and multiplanar MRI for whole-body detection of cancer metastases. J Magn Reson Imaging. 2009;29(5):1154–62.

    Article  PubMed  Google Scholar 

  61. Costelloe CM, Madewell JE, Kundra V, Harrell RK, Bassett RL, Ma J. Conspicuity of bone metastases on fast Dixon-based multisequence whole-body MRI: clinical utility per sequence. Magn Reson Imaging. 2013;31(5):669–75.

    Article  PubMed Central  PubMed  Google Scholar 

  62. Hu HH, Börnert P, Hernando D, Kellman P, Ma J, Reeder S, et al. ISMRM workshop on fat-water separation: insights, applications and progress in MRI. Magnetic Resonance in Medicine. 2012;68(2):378–88.

    Google Scholar 

  63. Hu HH, Nayak KS, Goran MI. Assessment of abdominal adipose tissue and organ fat content by magnetic resonance imaging. Obes Rev Off J Int Assoc Study Obes. 2011;12(5):e504–15.

    Article  CAS  Google Scholar 

  64. Reeder SB, Hu HH, Sirlin CB. Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging. 2012;36(5):1011–4.

    Article  PubMed  Google Scholar 

  65. St Pierre TG, Clark PR, Chua-anusorn W, Fleming AJ, Jeffrey GP, Olynyk JK, et al. Noninvasive measurement and imaging of liver iron concentrations using proton magnetic resonance. Blood. 2005;105(2):855–61.

    Article  CAS  PubMed  Google Scholar 

  66. Hernando D, Levin YS, Sirlin CB, Reeder SB. Quantification of liver iron with MRI: state of the art and remaining challenges. J Magn Reson Imaging. 2014. DOI: 10.1002/jmri.24584.

  67. Ouwerkerk R, Sodium MRI. Methods Mol Biol. 2011;711:175–201.

    Article  CAS  PubMed  Google Scholar 

  68. Wetterling F, Corteville DM, Kalayciyan R, Rennings A, Konstandin S, Nagel AM, et al. Whole body sodium MRI at 3T using an asymmetric birdcage resonator and short echo time sequence: first images of a male volunteer. Phys Med Biol. 2012;57(14):4555–67.

    Article  PubMed  Google Scholar 

  69. Kopp C, Linz P, Wachsmuth L, Dahlmann A, Horbach T, Schöfl C, et al. Na magnetic resonance imaging of tissue sodium. Hypertension. 2012;59(1):167–72.

    Article  CAS  PubMed  Google Scholar 

  70. Zuo CS, Villafuerte RA, Henry ME, Dobbins RL, Lee C, Sung Y, et al. MRI assessment of drug-induced fluid accumulation in humans: validation of the technology. Magn Reson Imaging. 2008;26(5):629–37.

    Article  CAS  PubMed  Google Scholar 

  71. Kopp C, Linz P, Dahlmann A, Hammon M, Jantsch J, Müller DN, et al. 23Na magnetic resonance imaging-determined tissue sodium in healthy subjects and hypertensive patients. Hypertension. 2013;61(3):635–40.

    Article  CAS  PubMed  Google Scholar 

  72. Madelin G, Regatte RR. Biomedical applications of sodium MRI in vivo. J Magn Reson Imaging. 2013;38(3):511–29.

    Article  PubMed Central  PubMed  Google Scholar 

  73. Mariappan YK, Glaser KJ, Ehman RL. Magnetic resonance elastography: a review. Clin Anat. 2010;23(5):497–511.

    Article  PubMed Central  PubMed  Google Scholar 

  74. Glaser KJ, Manduca A, Ehman RL. Review of MR elastography applications and recent developments. J Magn Reson Imaging. 2012;36(4):757–74.

    Article  PubMed  Google Scholar 

  75. Dresner MA, Rose GH, Rossman PJ, Muthupillai R, Manduca A, Ehman RL. Magnetic resonance elastography of skeletal muscle. J Magn Reson Imaging. 2001;13(2):269–76.

    Article  CAS  PubMed  Google Scholar 

  76. Ringleb SI, Bensamoun SF, Chen Q, Manduca A, An K-N, Ehman RL. Applications of magnetic resonance elastography to healthy and pathologic skeletal muscle. J Magn Reson Imaging. 2007;25(2):301–9.

    Article  PubMed  Google Scholar 

  77. Basford JR, Jenkyn TR, An K-N, Ehman RL, Heers G, Kaufman KR. Evaluation of healthy and diseased muscle with magnetic resonance elastography. Arch Phys Med Rehabil. 2002;83(11):1530–6.

    Article  PubMed  Google Scholar 

  78. Huwart L, Peeters F, Sinkus R, Annet L, Salameh N, ter Beek LC, et al. Liver fibrosis: non-invasive assessment with MR elastography. NMR Biomed. 2006;19(2):173–9.

    Article  PubMed  Google Scholar 

  79. Rouvière O, Yin M, Dresner MA, Rossman PJ, Burgart LJ, Fidler JL, et al. MR elastography of the liver: preliminary results. Radiology. 2006;240(2):440–8.

    Article  PubMed  Google Scholar 

  80. Yin M, Talwalkar JA, Glaser KJ, Manduca A, Grimm RC, Rossman PJ, et al. Assessment of hepatic fibrosis with magnetic resonance elastography. Clin Gastroenterol Hepatol. 2007;5(10):1207–13.e2.

    Article  PubMed Central  PubMed  Google Scholar 

  81. Huwart L, Salameh N, ter Beek L, Vicaut E, Peeters F, Sinkus R, et al. MR elastography of liver fibrosis: preliminary results comparing spin-echo and echo-planar imaging. Eur Radiol. 2008;18(11):2535–41.

    Article  PubMed  Google Scholar 

  82. Huwart L, Sempoux C, Vicaut E, Salameh N, Annet L, Danse E, et al. Magnetic resonance elastography for the noninvasive staging of liver fibrosis. Gastroenterology. 2008;135(1):32–40.

    Article  PubMed  Google Scholar 

  83. Do RKG, Rusinek H, Taouli B. Dynamic contrast-enhanced MR imaging of the liver: current status and future directions. Magn Reson Imaging Clin N Am. 2009;17(2):339–49.

    Article  PubMed  Google Scholar 

  84. Patel J, Sigmund EE, Rusinek H, Oei M, Babb JS, Taouli B. Diagnosis of cirrhosis with intravoxel incoherent motion diffusion MRI and dynamic contrast-enhanced MRI alone and in combination: preliminary experience. J Magn Reson Imaging. 2010;31(3):589–600.

    Article  PubMed  Google Scholar 

  85. Shim JH, Yu J-S, Chung J-J, Kim JH, Kim KW. Segmental difference of the hepatic fibrosis from chronic viral hepatitis due to hepatitis B versus C virus infection: comparison using dual contrast material-enhanced MRI. Korean J Radiol. 2011;12(4):431–8.

    Article  PubMed Central  PubMed  Google Scholar 

  86. Leporq B, Dumortier J, Pilleul F, Beuf O. 3D-liver perfusion MRI with the MS-325 blood pool agent: a noninvasive protocol to asses liver fibrosis. J Magn Reson Imaging. 2012;35(6):1380–7.

    Article  PubMed  Google Scholar 

  87. Chen B-B, Hsu C-Y, Yu C-W, Wei S-Y, Kao J-H, Lee H-S, et al. Dynamic contrast-enhanced magnetic resonance imaging with Gd-EOB-DTPA for the evaluation of liver fibrosis in chronic hepatitis patients. Eur Radiol. 2012;22(1):171–80.

    Article  PubMed  Google Scholar 

  88. Lewin M, Poujol-Robert A, Boëlle P-Y, Wendum D, Lasnier E, Viallon M, et al. Diffusion-weighted magnetic resonance imaging for the assessment of fibrosis in chronic hepatitis C. Hepatology. 2007;46(3):658–65.

    Article  CAS  PubMed  Google Scholar 

  89. Zhou M-L, Yan F-H, Xu P-J, Chen C-Z, Shen J-Z, Li R-C, et al. Comparative study on clinical and pathological changes of liver fibrosis with diffusion-weighted imaging. Zhonghua Yi Xue Za Zhi. 2009;89(25):1757–61.

    PubMed  Google Scholar 

  90. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology. 1988;168(2):497–505.

    Article  PubMed  Google Scholar 

  91. Dyvorne HA, Nevers T, Galea N, Fiel MI, Carpenter D, Wong E, et al. Intravoxel incoherent motion diffusion-weighted imaging for detection of liver fibrosis in HCV: comparison of four sequences. Proc Intl Soc Mag Reson Med. 20th Annual Meeting, Melbourne; 2012.

    Google Scholar 

  92. Longwei X. Clinical application of diffusion tensor magnetic resonance imaging in skeletal muscle. Muscles Ligaments Tendons J. 2012;2(1):19–24.

    PubMed Central  PubMed  Google Scholar 

  93. Scheel M, von Roth P, Winkler T, Arampatzis A, Prokscha T, Hamm B, et al. Fiber type characterization in skeletal muscle by diffusion tensor imaging. NMR Biomed. 2013;26(10):1220–4.

    Article  PubMed  Google Scholar 

  94. Hoy SM. Lorcaserin: a review of its use in chronic weight management. Drugs. 2013;73(5):463–73.

    Article  CAS  PubMed  Google Scholar 

  95. Rueda-Clausen CF, Padwal RS, Sharma AM. New pharmacological approaches for obesity management. Nat Rev Endocrinol. 2013;9(8):467–78.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mark Punyanitya MA, MS .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag London

About this chapter

Cite this chapter

Punyanitya, M., Clark, P.R. (2015). Assessment of Body Composition. In: Krentz, A., Heinemann, L., Hompesch, M. (eds) Translational Research Methods for Diabetes, Obesity and Cardiometabolic Drug Development. Springer, London. https://doi.org/10.1007/978-1-4471-4920-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4920-0_6

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4919-4

  • Online ISBN: 978-1-4471-4920-0

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics