Advertisement

Diagnosis of Osteosarcopenia – Imaging

  • Adam J. Kuchnia
  • Neil BinkleyEmail author
Chapter

Abstract

An essential requirement for translating osteosarcopenia research into clinical care is determining the best method for assessing both muscle and bone tissue composition. Several non-invasive imaging modalities exist that differ in terms of accuracy, reliability, cost, and radiation exposure. Magnetic resonance imaging (MRI), computed tomography (CT), dual energy X-ray absorptiometry (DXA), and ultrasound all have variable importance in assessing sarcopenia and osteoporosis, and may provide useful information for clinical decision making aimed at reducing falls, injuries and fractures. Recent advances to these technologies focus on measures that describe tissue ‘quality’, an important improvement beyond measurement of quantity, to better explain the gap in knowledge connecting muscle and bone with function, fracture, and mortality. Aligned with the growing interest in osteosarcopenia, this chapter explores the clinical applications and limitations of various imaging techniques available for the assessment of muscle and bone in the diagnosis of osteosarcopenia.

Keywords

Osteosarcopenia Osteoporosis Sarcopenia Dual-energy X-ray absorptiometry Muscle mass Magnetic resonance imaging Computed tomography Ultrasound Imaging 

References

  1. Abe T, Dabbs NC, Nahar VK et al (2013) Relationship between dual-energy X-ray absorptiometry-derived appendicular lean tissue mass and total body skeletal muscle mass estimated by ultrasound. Int J Clin Med 4:283–286CrossRefGoogle Scholar
  2. Abe T, Kondo M, Kawakami Y, Fukunaga T (1994) Prediction equations for body composition of Japanese adults by B-mode ultrasound. Am J Hum Biol 6:161–170.  https://doi.org/10.1002/ajhb.1310060204CrossRefPubMedGoogle Scholar
  3. Adams JE (2013) Advances in bone imaging for osteoporosis. Nat Rev Endocrinol 9:28–42.  https://doi.org/10.1038/nrendo.2012.217CrossRefPubMedGoogle Scholar
  4. Ahmed HU, El-Shater Bosaily A, Brown LC et al (2017) Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389:815–822.  https://doi.org/10.1016/S0140-6736(16)32401-1CrossRefPubMedGoogle Scholar
  5. Ai T, Morelli JN, Hu ÞX et al (2012) A historical overview of magnetic resonance imaging, focusing on technological innovations historical overview of magnetic resonance imaging, focusing on technological innovations. Investig Radiol 47:725–741CrossRefGoogle Scholar
  6. Arts IMP, Pillen S, Schelhaas HJ et al (2010) Normal values for quantitative muscle ultrasonography in adults. Muscle Nerve 41:32–41.  https://doi.org/10.1002/mus.21458CrossRefPubMedGoogle Scholar
  7. Balzano RF, Mattera M, Cheng X et al (2018) Osteoporosis: what the clinician needs to know? Quant Imaging Med Surg 8:39–46.  https://doi.org/10.21037/qims.2018.02.05CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bamber J, Cosgrove D, Dietrich C et al (2013) EFSUMB guidelines and recommendations on the clinical use of ultrasound Elastography. Part 1: basic principles and technology. Ultraschall der Med Eur J Ultrasound 34:169–184.  https://doi.org/10.1055/s-0033-1335205CrossRefGoogle Scholar
  9. Bauer D, Gluer C, Cauley J et al (1997) Broadband ultrasound attenuation predicts fractures strongly and independently of densitometry in older women. Arch Intern Med 157:629–634CrossRefGoogle Scholar
  10. Baumgartner RN, Koehler KM, Gallagher D et al (1998) Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol 147:755–763CrossRefGoogle Scholar
  11. Bazzocchi A, Ponti F, Albisinni U et al (2016) DXA: technical aspects and application. Eur J Radiol.  https://doi.org/10.1016/j.ejrad.2016.04.004CrossRefGoogle Scholar
  12. Bijlsma AY, Meskers CGM, Westendorp RGJ, Maier AB (2012) Chronology of age-related disease definitions: osteoporosis and sarcopenia. Ageing Res Rev 11:320–324.  https://doi.org/10.1016/j.arr.2012.01.001CrossRefPubMedGoogle Scholar
  13. Bley TA, Wieben O, Francois CJ et al (2010) Fat and water magnetic resonance imaging. J Magn Reson Imaging 31:4–18.  https://doi.org/10.1002/jmri.21895CrossRefPubMedGoogle Scholar
  14. Boehm HF, Vogel T, Panteleon A et al (2007) Differentiation between post-menopausal women with and without hip fractures: enhanced evaluation of clinical DXA by topological analysis of the mineral distribution in the scan images. Osteoporos Int 18:779–787.  https://doi.org/10.1007/s00198-006-0302-zCrossRefPubMedGoogle Scholar
  15. Borkan GA, Hults DE, Gerzof SG et al (1983) Age changes in body composition revealed by computed tomography. J Gerontol 38:673–677.  https://doi.org/10.1093/geronj/38.6.673CrossRefPubMedGoogle Scholar
  16. Boutin RD, Yao L, Canter RJ, Lenchik L (2015) Sarcopenia: current concepts and imaging implications. Am J Roentgenol 205:W255–W266.  https://doi.org/10.2214/AJR.15.14635CrossRefGoogle Scholar
  17. Brandenburg JE, Eby SF, Song P et al (2014) Ultrasound elastography: the new frontier in direct measurement of muscle stiffness. Arch Phys Med Rehabil 95:2207–2219.  https://doi.org/10.1016/j.apmr.2014.07.007CrossRefPubMedPubMedCentralGoogle Scholar
  18. Broy SB, Cauley JA, Lewiecki ME et al (2015) Fracture risk prediction by non-BMD DXA measures: the 2015 ISCD official positions part 1: hip geometry. J Clin Densitom 18:287–308.  https://doi.org/10.1016/j.jocd.2015.06.005CrossRefPubMedGoogle Scholar
  19. Cameron JR, Sorenson J (1963) Measurement of bone mineral in vivo: an improved method. Science (80– ) 142:230–232.  https://doi.org/10.1126/science.142.3589.230CrossRefGoogle Scholar
  20. Chang G, Rajapakse CS, Chen C et al (2018) 3-T MR imaging of proximal femur microarchitecture in subjects with and without fragility fracture and nonosteoporotic proximal femur bone mineral density. Radiology 287:608–619.  https://doi.org/10.1148/radiol.2017170138CrossRefPubMedPubMedCentralGoogle Scholar
  21. Chang JM, Moon WK, Cho N et al (2011) Clinical application of shear wave elastography (SWE) in the diagnosis of benign and malignant breast diseases. Breast Cancer Res Treat 129:89–97.  https://doi.org/10.1007/s10549-011-1627-7CrossRefPubMedGoogle Scholar
  22. Chen J, Grogan S, Shao H et al (2015) Evaluation of bound and pore water in cortical bone using ultrashort Echo time (UTE) magnetic resonance imaging. NMR Biomed 28:457–464.  https://doi.org/10.1097/COC.0b013e3182a79009.PainCrossRefGoogle Scholar
  23. Clotet J, Martelli Y, Di Gregorio S et al (2017) Structural parameters of the proximal femur by 3-dimensional dual-energy X-ray absorptiometry software: comparison with quantitative computed tomography. J Clin Densitom:1–13.  https://doi.org/10.1016/j.jocd.2017.05.002CrossRefGoogle Scholar
  24. Correa-de-Araujo R, Harris-Love MO, Miljkovic I et al (2017) The need for standardized assessment of muscle quality in skeletal muscle function deficit and other aging-related muscle dysfunctions: a symposium report. Front Physiol 8:1–19.  https://doi.org/10.3389/fphys.2017.00087CrossRefGoogle Scholar
  25. Cruz-Jentoft AJ, Baeyens JP, Bauer JM et al (2010) Sarcopenia: European consensus on definition and diagnosis. Age Ageing 39:412–423.  https://doi.org/10.1093/ageing/afq034CrossRefPubMedPubMedCentralGoogle Scholar
  26. Csapo R, Malis V, Sinha U et al (2014) Age-associated differences in triceps surae muscle composition and strength – an MRI-based cross-sectional comparison of contractile, adipose and connective tissue. BMC Musculoskelet Disord 15:209.  https://doi.org/10.1186/1471-2474-15-209CrossRefPubMedPubMedCentralGoogle Scholar
  27. Damadian R (1971) Tumor detection by nuclear magnetic resonance. Science (80– ) 171:1151–1153CrossRefGoogle Scholar
  28. Delmonico MJ, Harris TB, Visser M et al (2009) Longitudinal study of muscle strength, quality, and adipose tissue infiltration. Am J Clin Nutr 90:1579–1585.  https://doi.org/10.3945/ajcn.2009.28047.INTRODUCTIONCrossRefPubMedPubMedCentralGoogle Scholar
  29. Eby SF, Cloud BA, Brandenburg JE et al (2015) Shear wave elastography of passive skeletal muscle stiffness: influences of sex and age throughout adulthood. Clin Biomech 30:22–27.  https://doi.org/10.1016/j.clinbiomech.2014.11.011CrossRefGoogle Scholar
  30. Engelke K, Adams JE, Armbrecht G et al (2008) Clinical use of quantitative computed tomography and peripheral quantitative computed tomography in the management of osteoporosis in adults: the 2007 ISCD Official Positions. J Clin Densitom 11:123–162.  https://doi.org/10.1016/j.jocd.2007.12.010CrossRefPubMedGoogle Scholar
  31. Filho JCA, Pinheiro MM, de Moura Castro CH, Szejnfeld VL (2013) Prevalence and risk factors associated with low-impact fractures in men with rheumatoid arthritis. Clin Rheumatol 33:1389–1395.  https://doi.org/10.1007/s10067-013-2426-9CrossRefPubMedGoogle Scholar
  32. Foster MA, Hutchison JM, Mallard JR, Fuller M (1984) Nuclear magnetic resonance pulse sequence and discrimination of high- and low-fat tissues. Magn Reson Imaging 2:187–192CrossRefGoogle Scholar
  33. Galbán CJ, Maderwald S, Stock F, Ladd ME (2007) Age-related changes in skeletal muscle as detected by diffusion tensor magnetic resonance imaging. J Gerontol Ser A Biol Sci Med Sci 62:453–458.  https://doi.org/10.1093/gerona/62.4.453CrossRefGoogle Scholar
  34. Gluer CC, Wu CY, Jergas M et al (1994) Three quantitative ultrasound parameters reflect bone structure. Calcif Tissue Int 55:46–52.  https://doi.org/10.1007/BF00310168CrossRefPubMedGoogle Scholar
  35. Goodpaster BH, Thaete FL, Kelley DE (2000a) Composition of skeletal muscle evaluated with computed tomography. Vivo Body Comosition Stud 904:18–24Google Scholar
  36. Goodpaster BH, Thaete FL, Kelley DE (2000b) Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus. Am J Clin Nutr 71:885–892CrossRefGoogle Scholar
  37. Graffy PM, Lee SJ, Ziemlewicz TJ, Pickhardt PJ (2017) Prevalence of vertebral compression fractures on routine ct scans according to l1 trabecular attenuation: determining relevant thresholds for opportunistic osteoporosis screening. Am J Roentgenol 209:491–496.  https://doi.org/10.2214/AJR.17.17853CrossRefGoogle Scholar
  38. Guerri S, Mercatelli D, Aparisi Gómez MP et al (2018) Quantitative imaging techniques for the assessment of osteoporosis and sarcopenia. Quant Imaging Med Surg 8:60–85.  https://doi.org/10.21037/qims.2018.01.05CrossRefPubMedPubMedCentralGoogle Scholar
  39. Guglielmi G, de Terlizzi F (2009) Quantitative ultrasound in the assessment of osteoporosis. Eur J Radiol 71:425–431.  https://doi.org/10.1016/j.ejrad.2008.04.060CrossRefPubMedGoogle Scholar
  40. Guglielmi G, Muscarella S, Bazzocchi A (2011) Integrated imaging approach to osteoporosis: state-of-the-art review and update. Musculoskelet Imaging 31:1343–1364Google Scholar
  41. Hans D, Baim S (2017) Quantitative ultrasound (QUS) in the management of osteoporosis and assessment of fracture risk. J Clin Densitom 20:322–333.  https://doi.org/10.1016/j.jocd.2017.06.018CrossRefPubMedGoogle Scholar
  42. Hans D, Goertzen AL, Krieg MA, Leslie WD (2011) Bone microarchitecture assessed by TBS predicts osteoporotic fractures independent of bone density: the Manitoba study. J Bone Miner Res 26:2762–2769.  https://doi.org/10.1002/jbmr.499CrossRefPubMedGoogle Scholar
  43. Harris-Love MO, Avila NA, Adams B et al (2018) The comparative associations of ultrasound and computed tomography estimates of muscle quality with physical performance and metabolic parameters in older men. J Clin Med 7.  https://doi.org/10.3390/jcm7100340CrossRefGoogle Scholar
  44. Harris-Love MO, Ismail C, Monfaredi R et al (2016) Interrater reliability of quantitative ultrasound using force feedback among examiners with varied levels of experience. Peer J 4:e2146.  https://doi.org/10.7717/peerj.2146CrossRefPubMedGoogle Scholar
  45. Harris-Love MO, Monfaredi R, Ismail C et al (2014) Quantitative ultrasound: measurement considerations for the assessment of muscular dystrophy and sarcopenia. Front Aging Neurosci 6.  https://doi.org/10.3389/fnagi.2014.00172
  46. Harvey N, Gluer C, Binkley N et al (2015) Trabecular bone score (TBS) as a new complementary approach for osteoporosis evaluation in clinical practice: a consensus report of a European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) Working Group. Bone 78:216–224.  https://doi.org/10.1016/j.bone.2015.05.016.TrabecularCrossRefPubMedPubMedCentralGoogle Scholar
  47. Hendrickson NR, Pickhardt PJ, Munoz A et al (2018) Bone mineral density T-scores derived from CT attenuation numbers (Hounsfield units): clinical utility and correlation with dual-energy X-ray absorptiometry. Iowa Orthop J 38:25–31PubMedPubMedCentralGoogle Scholar
  48. Heymsfield S, Olafson R, Kutner M, Nixon D (1979) A radiographic mehtod of quantifying protein-calorie undernutrition. Am J Clin Nutr 32:693–702CrossRefGoogle Scholar
  49. Heymsfield SB, Gonzalez MC, Lu J et al (2015) Skeletal muscle mass and quality: evolution of modern measurement concepts in the context of sarcopenia. Proc Nutr Soc 74:355–366.  https://doi.org/10.1017/S0029665115000129CrossRefPubMedGoogle Scholar
  50. Heymsfield SB, Wang Z, York N et al (1997) Human body composition: advances in models and methods. Annu Rev Nutr 17:527–558CrossRefGoogle Scholar
  51. Hounsfield GN (1973) Computerized transverse axial scanning (tomography). 1. Description of system. Br J Radiol 46:1016–1022.  https://doi.org/10.1259/0007-1285-46-552-1016CrossRefPubMedGoogle Scholar
  52. Hu H, Kan H (2013) Quantitative proton magnetic resonance techniques for measuring fat. NMR Biomed 26:1609–1629.  https://doi.org/10.1002/nbm.3025.QuantitativeCrossRefPubMedGoogle Scholar
  53. Humbert L, Martelli Y, Fonolla R et al (2017) 3D-DXA: assessing the femoral shape, the trabecular macrostructure and the cortex in 3D from DXA images. IEEE Trans Med Imaging 36:27–39.  https://doi.org/10.1109/TMI.2016.2593346CrossRefPubMedGoogle Scholar
  54. Campbell IT, Watt T, Withers D et al (1995) Muscle thickness, measured with ultrasound, may be an indicator of lean tissue wasting in multiple organ failure in the presence of edema. Am J Clin Nutr 62:533–539CrossRefGoogle Scholar
  55. Jerban S, Ma Y, Nazaran A et al (2018) Detecting stress injury (fatigue fracture) in fibular cortical bone using quantitative ultrashort echo time-magnetization transfer (UTE-MT): an ex vivo study. NMR Biomed 31:e3994.  https://doi.org/10.1002/nbm.3994CrossRefPubMedPubMedCentralGoogle Scholar
  56. Kaunitz JD (2018) Magnetic resonance imaging: the nuclear option. Dig Dis Sci 63:1100–1101.  https://doi.org/10.1007/s10620-018-4992-9CrossRefPubMedPubMedCentralGoogle Scholar
  57. Keeler EK, Giambalvo A, Smith SD, Negendank W (1983) Initial assessment of the performance of an 0.3 T permanent magnet in whole body NMR imaging. Physiol Chem Phys Med NMR 15:319–335PubMedGoogle Scholar
  58. Kim J, Wang Z, Heymsfield SB et al (2002) Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method. Am J Clin Nutr 76:378–383.  https://doi.org/10.1093/ajcn/76.2.378CrossRefPubMedGoogle Scholar
  59. Kim TY, Schafer AL (2016) Diabetes and bone marrow adiposity Tiffany. Curr Osteoporos Rep 14:337–344.  https://doi.org/10.1007/s11914-016-0336-x.DiabetesCrossRefPubMedPubMedCentralGoogle Scholar
  60. Koppaka S, Gilbertson MW, Rutkove SB, Anthony BW (2014) Evaluating the Clinical Relevance of Force-Correlated Ultrasound. In: IEEE 11th International Symposium on Biomedical Imaging (ISBI). Beijing: Institute of Electrical and Electronics Engineers (IEEE):1172–1175.  https://doi.org/10.1109/ISBI.2014.6868084
  61. Krieg MA, Barkmann R, Gonnelli S et al (2008) Quantitative ultrasound in the management of osteoporosis: the 2007 ISCD Official Positions. J Clin Densitom 11:163–187.  https://doi.org/10.1016/j.jocd.2007.12.011CrossRefPubMedGoogle Scholar
  62. Kuchnia A, Earthman C, Teigen L et al (2016) Evaluation of bioelectrical impedance analysis in critically ill patients: results of a multicenter prospective study. J Parenter Enter Nutr 41:1131–1138.  https://doi.org/10.1177/0148607116651063CrossRefGoogle Scholar
  63. Kuchnia AJ, Yamada Y, Teigen L et al (2018) Combination of DXA and BIS body composition measurements is highly correlated with physical function—an approach to improve muscle mass assessment. Arch Osteoporos 13:97CrossRefGoogle Scholar
  64. Kyle UG, Bosaeus I, De Lorenzo AD et al (2004) Bioelectrical impedance analysis – part I: review of principles and methods. Clin Nutr 23:1226–1243.  https://doi.org/10.1016/j.clnu.2004.06.004CrossRefPubMedGoogle Scholar
  65. Langton M, Palmer SB, Porter RW (1984) The measurement of broadband ultrasonic attenuation in cancellous bone. Eng Med 13:89–91CrossRefGoogle Scholar
  66. Lee SJ, Graffy PM, Zea RD et al (2018) Future osteoporotic fracture risk related to lumbar vertebral trabecular attenuation measured at routine body CT. J Bone Miner Res 33:860–867.  https://doi.org/10.1002/jbmr.3383CrossRefPubMedPubMedCentralGoogle Scholar
  67. Link TM (2012) Osteoporosis imaging: state of the art and advanced imaging. Radiology 263:3A–4A.  https://doi.org/10.1148/radiol.2633201203CrossRefGoogle Scholar
  68. Lustgarten MS, Fielding RA (2011) Assessment of analytical methods used to measure changes in body composition in the elderly and recommendations for their use in phase II clinical trials. J Nutr Health Aging 15:368–375.  https://doi.org/10.1007/s12603-011-0049-xCrossRefPubMedPubMedCentralGoogle Scholar
  69. Mack P, O’Brien A, Smith J, Bauman A (1939) A method for estimating the degree of mineralization of bones from tracings of roentgenograms. Science (80– ) 89:467CrossRefGoogle Scholar
  70. Manhard M, Nyman J, Does M (2017) Advances in imaging approaches to fracture risk evaluation. Transl Res J Lab Clin Med 181:1–14.  https://doi.org/10.1038/nrg3575.SystemsCrossRefGoogle Scholar
  71. Matthie JR (2008) Bioimpedance measurements of human body composition: critical analysis and outlook. Expert Rev Med Devices 5:239–261.  https://doi.org/10.1586/17434440.5.2.239CrossRefPubMedGoogle Scholar
  72. Mazess R, Cameron J, Sorenson J (1970) Determining body composition by radiation absorption spectrometry. Nature 228:771–772.  https://doi.org/10.1038/228549a0CrossRefPubMedGoogle Scholar
  73. McCloskey EV, Odén A, Harvey NC et al (2016) A meta-analysis of trabecular bone score in fracture risk prediction and its relationship to FRAX. J Bone Miner Res 31:940–948.  https://doi.org/10.1002/jbmr.2734CrossRefPubMedGoogle Scholar
  74. McRobbie D, Moore E, Graves M, Prince M (2007) MRI from picture to proton, 2nd edn. Cambridge University Press, CambridgeGoogle Scholar
  75. Messina C, Maffi G, Vitale JA et al (2018) Diagnostic imaging of osteoporosis and sarcopenia: a narrative review. Quant Imaging Med Surg 8:86–99.  https://doi.org/10.21037/qims.2018.01.01CrossRefPubMedPubMedCentralGoogle Scholar
  76. Michael Lewiecki E, Binkley N (2017) DXA: 30 years and counting: introduction to the 30th anniversary issue. Bone 104:1–3.  https://doi.org/10.1016/j.bone.2016.12.013CrossRefPubMedGoogle Scholar
  77. Mourtzakis M, Prado CMM, Lieffers JR et al (2008) A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab 33:997–1006.  https://doi.org/10.1139/H08-075CrossRefPubMedGoogle Scholar
  78. Nescolarde L, Yanguas J, Lukaski H et al (2015) Effects of muscle injury severity on localized bioimpedance measurements. Physiol Meas 36:27–42.  https://doi.org/10.1088/0967-3334/36/1/27CrossRefPubMedGoogle Scholar
  79. Oftadeh R, Perez-Viloria M, Villa-Camacho JC et al (2015) Biomechanics and mechanobiology of trabecular bone: a review. J Biomech Eng 137:010802.  https://doi.org/10.1115/1.4029176CrossRefGoogle Scholar
  80. Oo WM, Naganathan V, Bo MT, Hunter DJ (2018) Clinical utilities of quantitative ultrasound in osteoporosis associated with inflammatory rheumatic diseases. Quant Imaging Med Surg 8:100–113.  https://doi.org/10.21037/qims.2018.02.02CrossRefPubMedPubMedCentralGoogle Scholar
  81. Paris MT, Lafleur B, Dubin JA, Mourtzakis M (2017a) Development of a bedside viable ultrasound protocol to quantify appendicular lean tissue mass. J Cachexia Sarcopenia Muscle.  https://doi.org/10.1002/jcsm.12213CrossRefGoogle Scholar
  82. Paris MT, Mourtzakis M, Day A et al (2017b) Validation of bedside ultrasound of muscle layer thickness of the quadriceps in the critically ill patient (VALIDUM study). J Parenter Enter Nutr 41:171–180.  https://doi.org/10.1177/0148607116637852CrossRefGoogle Scholar
  83. Pickhardt PJ, Pooler BD, Lauder T et al (2013) Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med 158:588–595.  https://doi.org/10.7326/0003-4819-158-8-201304160-00003CrossRefPubMedPubMedCentralGoogle Scholar
  84. Pietrobelli A, Formica C, Wang Z, Heymsfield SB (1996) Dual-energy X-ray absorptiometry body composition model: review of physical concepts. Am J Phys 271:E941–E951Google Scholar
  85. Pillen S, van Alfen N (2011) Skeletal muscle ultrasound. Neurol Res 33:1016–1024.  https://doi.org/10.1179/1743132811Y.0000000010CrossRefPubMedGoogle Scholar
  86. Prado CM, Lieffers JR, McCargar LJ et al (2008) Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 9:629–635.  https://doi.org/10.1016/S1470-2045(08)70153-0CrossRefPubMedGoogle Scholar
  87. Puntmann VO, Carr-White G, Jabbour A et al (2016) T1-mapping and outcome in nonischemic cardiomyopathy. JACC Cardiovasc Imaging 9:40–50.  https://doi.org/10.1016/j.jcmg.2015.12.001CrossRefPubMedGoogle Scholar
  88. Radue E, Weigel M, Wiest R, Urbach H (2016) Introduction to magnetic resonance imaging for neurologists. Continuum (New York) 22:1379–1398.  https://doi.org/10.1212/CON.0000000000000391CrossRefGoogle Scholar
  89. Reeder S, Sirlin M (2010) Quantification of liver fat with magnetic resonance imaging. Magn Reson Imaging Clin J 18:1–34.  https://doi.org/10.1016/j.mric.2010.08.013.QuantificationCrossRefGoogle Scholar
  90. Reeder SB, Hu HH, Sirlin CB et al (2012) Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 36:1011–1014.  https://doi.org/10.1002/jmri.23741.ProtonCrossRefPubMedPubMedCentralGoogle Scholar
  91. Reimers CD, Fleckenstein JL, Witt TN et al (1993a) Muscular ultrasound in idiopathic inflammatory myopathies of adults. J Neurol Sci 116:82–92.  https://doi.org/10.1016/0022-510X(93)90093-ECrossRefPubMedGoogle Scholar
  92. Reimers K, Reimers CD, Wagner S et al (1993b) Skeletal muscle sonography: a correlative study of echogenicity and morphology. J Ultrasound Med 12:73–77CrossRefGoogle Scholar
  93. Ripamonti C, Lisi L, Buffa A et al (2018) The trabecular bone score predicts spine fragility fractures in postmenopausal Caucasian women without osteoporosis independently of bone mineral density. Med Arch 72:46.  https://doi.org/10.5455/medarh.2018.72.46-50CrossRefPubMedPubMedCentralGoogle Scholar
  94. Rosenberg IH (1989) Summary comments. Am J Clin Nutr 50:1231–1233CrossRefGoogle Scholar
  95. Sanada K, Kearns CF, Midorikawa T, Abe T (2006) Prediction and validation of total and regional skeletal muscle mass by ultrasound in Japanese adults. Eur J Appl Physiol 96:24–31.  https://doi.org/10.1007/s00421-005-0061-0CrossRefPubMedGoogle Scholar
  96. Schild H (1990) MRI Made Easy. Berlin: Berlex LaboratoriesGoogle Scholar
  97. Schuit SCE, Van Der Klift M, Weel AEAM et al (2004) Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone 34:195–202.  https://doi.org/10.1016/j.bone.2003.10.001CrossRefPubMedGoogle Scholar
  98. Schwenzer NF, Martirosian P, Machann J et al (2009) Aging effects on human calf muscle properties assessed by MRI at 3 Tesla. J Magn Reson Imaging 29:1346–1354.  https://doi.org/10.1002/jmri.21789CrossRefPubMedGoogle Scholar
  99. Shen W, Punyanitya M, Wang Z et al (2004) Total Body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol 97:2333–2338.  https://doi.org/10.1152/japplphysiol.00744.2004CrossRefPubMedGoogle Scholar
  100. Siglinsky E, Buehring B, Krueger D et al (2018) Could bioelectric impedance spectroscopy (BIS) measured appendicular intracellular water serve as a lean mass measurement in sarcopenia definitions? A pilot study. Osteoporos Int 29:1653–1657.  https://doi.org/10.1007/s00198-018-4475-zCrossRefPubMedGoogle Scholar
  101. Silva BC, Leslie WD, Resch H et al (2014) Trabecular bone score: a noninvasive analytical method based upon the DXA image. J Bone Miner Res 29:518–530.  https://doi.org/10.1002/jbmr.2176CrossRefPubMedGoogle Scholar
  102. Sjøblom B, Grønberg BH, Wentzel-Larsen T et al (2016) Skeletal muscle radiodensity is prognostic for survival in patients with advanced non-small cell lung cancer. Clin Nutr.  https://doi.org/10.1016/j.clnu.2016.03.010CrossRefGoogle Scholar
  103. St-Onge M-PP, Wang Z, Horlick M et al (2004) Dual-energy X-ray absorptiometry lean soft tissue hydration: independent contributions of intra- and extracellular water. Am J Physiol Endocrinol Metab 287:E842–E847.  https://doi.org/10.1152/ajpendo.00361.2003CrossRefPubMedGoogle Scholar
  104. Stuart HC, Dwinell PH (1942) The growth of bone, muscle and overlying tissues in children six to ten years of age as revealed by studies of roentgenograms of the leg area. Child Dev 13:195.  https://doi.org/10.2307/1125857CrossRefGoogle Scholar
  105. Studenski SA, Peters KW, Alley DE et al (2014) The FNIH sarcopenia project: rationale, study description, conference recommendations, and final estimates. J Gerontol Ser A Biol Sci Med Sci 69(A):547–558.  https://doi.org/10.1093/gerona/glu010CrossRefGoogle Scholar
  106. Takai Y, Ohta M, Akagi R et al (2014) Applicability of ultrasound muscle thickness measurements for predicting fat-free mass in elderly population. J Nutr Health Aging 18:579–585.  https://doi.org/10.1007/s12603-013-0419-7CrossRefPubMedGoogle Scholar
  107. Tang GY, Lv ZW, Tang RB et al (2010) Evaluation of MR spectroscopy and diffusion-weighted MRI in detecting bone marrow changes in postmenopausal women with osteoporosis. Clin Radiol 65:377–381.  https://doi.org/10.1016/j.crad.2009.12.011CrossRefPubMedGoogle Scholar
  108. La Tegola L, Mattera M, Cornacchia S et al (2018) Diagnostic imaging of two related chronic diseases: sarcopenia and osteoporosis. J Frailty Sarcopenia Falls 3:138–147.  https://doi.org/10.22540/JFSF-03-138CrossRefGoogle Scholar
  109. Teigen LM, John R, Kuchnia AJ et al (2017a) Preoperative pectoralis muscle quantity and attenuation by computed tomography are novel and powerful predictors of mortality after left ventricular assist device implantation. Circ Heart Fail 10.  https://doi.org/10.1161/CIRCHEARTFAILURE.117.004069
  110. Teigen LM, Kuchnia AJ, Mourtzakis M, Earthman CP (2017b) The use of technology for estimating body composition: strengths and weaknesses of common modalities in a clinical setting. Nutr Clin Pract 32:20–27.  https://doi.org/10.1177/0884533616676264CrossRefPubMedGoogle Scholar
  111. Tokunaga K, Matsuzawa Y, Ishikawa K, Tarui S (1983) A novel technique for the determination of body fat by computed tomography. Int J Obes 7:437–445.  https://doi.org/10.2307/40541591CrossRefPubMedGoogle Scholar
  112. Visser M, Goodpaster BH, Kritchevsky SB et al (2005) Muscle mass, muscle strength, and muscle fat infiltration as predictors of incident mobility limitations in well-functioning older persons. J Gerontol A Biol Sci Med Sci 60:324–333.  https://doi.org/10.1093/gerona/60.3.324CrossRefPubMedGoogle Scholar
  113. Wang Z-M, Pierson RN, Heymsfield S (1992) The five-level model: a new approach to organizing. Am J Clin Nutr 56:19–28CrossRefGoogle Scholar
  114. Wells PNT (2005) Sir Godfrey Newbold Hounsfield KT CBE. 28 August 1919–12 August 2004: elected F.R.S. 1975. Biogr Mem Fellows R Soc 51:221–235.  https://doi.org/10.1098/rsbm.2005.0014CrossRefGoogle Scholar
  115. Whitmarsh T, Fritscher KD, Humbert L et al (2012) Hip fracture discrimination from dual-energy X-ray absorptiometry by statistical model registration. Bone 51:896–901.  https://doi.org/10.1016/j.bone.2012.08.114CrossRefPubMedGoogle Scholar
  116. Yamada Y, Schoeller DA, Nakamura E et al (2010) Extracellular water may mask actual muscle atrophy during aging. J Gerontol Ser A Biol Sci Med Sci 65A:510–516.  https://doi.org/10.1093/gerona/glq001CrossRefGoogle Scholar
  117. Yamada Y, Yoshida T, Yokoyama K et al (2016) The extracellular to intracellular water ratio in upper legs is negatively associated with skeletal muscle strength and gait speed in older people. J Gerontol Ser A Biol Sci Med Sci 72:293–298.  https://doi.org/10.1093/gerona/glw125CrossRefGoogle Scholar
  118. Yeung DKW, Griffith JF, Antonio GE et al (2005) Osteoporosis is associated with increased marrow fat content and decreased marrow fat unsaturation: a proton MR spectroscopy study. J Magn Reson Imaging 22:279–285.  https://doi.org/10.1002/jmri.20367CrossRefPubMedGoogle Scholar
  119. Yoon JH, Lee JM, Joo I et al (2014) Hepatic fibrosis: prospective comparison of MR Elastography and US shear wave elastography for evaluation. Radiology 273:132000.  https://doi.org/10.1148/radiol.14132000CrossRefGoogle Scholar
  120. Young H, Jenkins N, Zhao Q, McCully K (2015) Measurement of intramuscular fat by muscle Echo intensity. Muscle Nerve 52:95–121.  https://doi.org/10.1007/128CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of WisconsinMadisonUSA

Personalised recommendations