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Cited by 102Body Composition Measurement TechniquesAdvanced MRI Techniques and ApplicationsNutrition and Health in Aging

MRI in the assessment of adipose tissues and muscle composition: how to use it

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Huber, Florian Alexander, Del Grande, Filippo, Rizzo, Stefania, Guglielmi, Giuseppe, Guggenberger, Roman·2020·Quantitative Imaging in Medicine and Surgery
DOI 10.21037/qims.2020.02.06PMC PMC7378099
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Abstract

Body composition analysis based on the characterization of different tissue compartments is currently experiencing increasing attention by a broad range of medical disciplines for both clinical and research questions. However, body composition profiling (BCP) can be performed utilizing different modalities, which all come along with several technical and diagnostic strengths and limitations, respectively. Magnetic resonance imaging (MRI) demonstrates good soft tissue resolution, high contrast between fat and water, and is free from ionizing radiation. This review article represents an overview of imaging techniques for body composition assessment, focussing on qualitative and quantitative methods of assessing adipose tissue and muscles in MRI.

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  • MRI can be considered a gold-standard method for accuracy, but MRI is not routinely available for assessment of muscle mass.

    “Magnetic resonance imaging (MRI) can be considered a gold‐standard method for accuracy, 42 but MRI is not routinely available for the assessment of muscle mass. 40 , 41 , 42”

    From GLIM consensus approach to diagnosis of malnutrition: A 5‐year update (2025) · cites this paper as [42]

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  1. Borga M, West J, Bell JD, Harvey NC, Romu T, Heymsfield SB, Dahlqvist Leinhard O. Advanced body composition assessment: from body mass index to body composition profiling. J Investig Med 2018;66:1-9.
  2. Fosbøl MØ, Zerahn B. Contemporary methods of body composition measurement. Clin Physiol Funct Imaging 2015;35:81-97.
  3. Grauer WO, Moss AA, Cann CE, Goldberg HI. Quantification of body fat distribution in the abdomen using computed tomography. Am J Clin Nutr 1984;39:631-7.
  4. Tosato M, Marzetti E, Cesari M, Savera G, Miller RR, Bernabei R, Landi F, Calvani R. Measurement of muscle mass in sarcopenia: from imaging to biochemical markers. Aging Clin Exp Res 2017;29:19-27.
  5. Sergi G, Trevisan C, Veronese N, Lucato P, Manzato E. Imaging of sarcopenia. Eur J Radiol 2016;85:1519-24.
  6. Nana A, Slater GJ, Stewart AD, Burke LM. Methodology review: using dual-energy X-ray absorptiometry (DXA) for the assessment of body composition in athletes and active people. Int J Sport Nutr Exerc Metab 2015;25:198-215.
  7. Santos DA, Dawson JA, Matias CN, Rocha PM, Minderico CS, Allison DB, Sardinha LB, Silva AM. Reference values for body composition and anthropometric measurements in athletes. PLoS One 2014;9:e97846.
  8. Moore FD, Boyden CM. Body cell mass and limits of hydration of the fat-free body: Their relation to estimated skeletal weight. Ann N Y Acad Sci 1963;110:62-71.
  9. Horber FF, Thomi F, Casez JP, Fonteille J, Jaeger P. Impact of hydration status on body composition as measured by dual energy X-ray absorptiometry in normal volunteers and patients on haemodialysis. Br J Radiol 1992;65:895-900.
  10. 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:E736-45.
  11. Lenchik L, Boutin RD. Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning. Semin Musculoskelet Radiol 2018;22:307-22.
  12. Burns JE, Yao J, Chalhoub D, Chen JJ, Summers RM. A Machine Learning Algorithm to Estimate Sarcopenia on Abdominal CT. Acad Radiol 2020;27:311-20.
  13. Woodrow G. Body composition analysis techniques in the aged adult: indications and limitations. Curr Opin Clin Nutr Metab Care 2009;12:8-14.
  14. Selberg O, Burchert W, Graubner G, Wenner C, Ehrenheim C, Müller MJ. Determination of anatomical skeletal muscle mass by whole body nuclear magnetic resonance. Basic Life Sci 1993;60:95-7.
  15. Lustgarten MS, Fielding RA. 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 2011;15:368-75.
  16. Nordez A, Jolivet E, Südhoff I, Bonneau D, de Guise JA, Skalli W. Comparison of methods to assess quadriceps muscle volume using magnetic resonance imaging. J Magn Reson Imaging 2009;30:1116-23.
  17. Yoo YH, Kim HS, Lee YH, Yoon CS, Paek MY, Yoo H, Kannengiesser S, Chung TS, Song HT, Suh JS, Kim S. Comparison of Multi-Echo Dixon Methods with Volume Interpolated Breath-Hold Gradient Echo Magnetic Resonance Imaging in Fat-Signal Fraction Quantification of Paravertebral Muscle. Korean J Radiol 2015;16:1086-95.
  18. Lenchik L, Heacock L, Weaver AA, Boutin RD, Cook TS, Itri J, Filippi CG, Gullapalli RP, Lee J, Zagurovskaya M, Retson T, Godwin K, Nicholson J, Narayana PA. Automated Segmentation of Tissues Using CT and MRI: A Systematic Review. Acad Radiol 2019;26:1695-706.
  19. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion- Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012;30:1323-41.
  20. Borga M. MRI adipose tissue and muscle composition analysis-a review of automation techniques. Br J Radiol 2018;91:20180252.
  21. Herfkens R, Davis P, Crooks L, Kaufman L, Price D, © Quantitative Imaging in Medicine and Surgery. All rights reserved. Quant Imaging Med Surg 2020;10(8):1636-1649 | http://dx.doi.org/10.21037/qims.2020.02.06
  22. Miller T, Margulis AR, Watts J, Hoenninger J, Arakawa M, McRee R. Nuclear magnetic resonance imaging of the abnormal live rat and correlations with tissue characteristics. Radiology 1981;141:211-8.
  23. Jungmann PM, Baum T, Nevitt MC, Nardo L, Gersing AS, Lane NE, McCulloch CE, Rummeny EJ, Link TM. Degeneration in ACL Injured Knees with and without Reconstruction in Relation to Muscle Size and Fat Content-Data from the Osteoarthritis Initiative. PLoS One 2016;11:e0166865.
  24. Alizai H, Nardo L, Karampinos DC, Joseph GB, Yap SP, Baum T, Krug R, Majumdar S, Link TM. Comparison of clinical semi-quantitative assessment of muscle fat infiltration with quantitative assessment using chemical shift-based water/fat separation in MR studies of the calf of post-menopausal women. Eur Radiol 2012;22:1592-600.
  25. Milisenda JC, Collado MV, Pinal-Fernandez I, Hormaza Jaramillo A, Faruch Bilfeld M, Cano MD, García AI, Tomás X, Grau JM. Correlation between quantitative and semiquantitative magnetic resonance imaging and histopathology findings in dermatomyositis. Clin Exp Rheumatol 2019;37:633-40.
  26. Cheung S, Dillon E, Tham SC, Feeley BT, Link TM, Steinbach L, Ma CB. The presence of fatty infiltration in the infraspinatus: its relation with the condition of the supraspinatus tendon. Arthroscopy 2011;27:463-70.
  27. Davis DL, Kesler T, Gilotra MN, Almardawi R, Hasan SA, Gullapalli RP, Zhuo J. Quantification of shoulder muscle intramuscular fatty infiltration on T1-weighted MRI: a viable alternative to the Goutallier classification system. Skeletal Radiol 2019;48:535-41.
  28. Kuno S, Katsuta S, Inouye T, Anno I, Matsumoto K, Akisada M. Relationship between MR relaxation time and muscle fiber composition. Radiology 1988;169:567-8.
  29. Budzik JF, Balbi V, Verclytte S, Pansini V, Le Thuc V, Cotten A. Diffusion tensor imaging in musculoskeletal disorders. Radiographics 2014;34:E56-72.
  30. Galbán CJ, Maderwald S, Stock F, Ladd ME. Age-related changes in skeletal muscle as detected by diffusion tensor magnetic resonance imaging. J Gerontol A Biol Sci Med Sci 2007;62:453-8.
  31. Hata J, Nakashima D, Tsuji O, Fujiyoshi K, Yasutake K, Sera Y, Komaki Y, Hikishima K, Nagura T, Matsumoto M, Okano H, Nakamura M. Noninvasive technique to evaluate the muscle fiber characteristics using q-space imaging. PLoS One 2019;14:e0214805.
  32. Dixon WT. Simple proton spectroscopic imaging. Radiology 1984;153:189-94.
  33. Pasoglou V, Michoux N, Larbi A, Van Nieuwenhove S, Lecouvet F. Whole Body MRI and oncology: recent major advances. Br J Radiol 2018;91:20170664.
  34. Bray TJP, Singh S, Latifoltojar A, Rajesparan K, Rahman F, Narayanan P, Naaseri S, Lopes A, Bainbridge A, Punwani S, Hall-Craggs MA. Diagnostic utility of whole body Dixon MRI in multiple myeloma: A multi-reader study. PLoS One 2017;12:e0180562.
  35. Ulbrich EJ, Nanz D, Leinhard OD, Marcon M, Fischer MA. Whole-body adipose tissue and lean muscle volumes and their distribution across gender and age: MR-derived normative values in a normal-weight Swiss population. Magn Reson Med 2018;79:449-58.
  36. Ma J. Dixon techniques for water and fat imaging. J Magn Reson Imaging 2008;28:543-58.
  37. Kellman P, Hernando D, Shah S, Zuehlsdorff S, Jerecic R, Mancini C, Liang ZP, Arai AE. Multiecho dixon fat and water separation method for detecting fibrofatty infiltration in the myocardium. Magn Reson Med 2009;61:215-21.
  38. Heiken JP, Lee JK, Dixon WT. Fatty infiltration of the liver: evaluation by proton spectroscopic imaging. Radiology 2019;47:3080-8.
  39. Hayashi T, Saitoh S, Takahashi J, Tsuji Y, Ikeda K, Kobayashi M, Kawamura Y, Fujii T, Inoue M, Miyati T, Kumada H. Hepatic fat quantification using the two-point Dixon method and fat color maps based on non-alcoholic fatty liver disease activity score. Hepatol Res 2017;47:455-64.
  40. Andersson T, Romu T, Karlsson A, Norén B, Forsgren MF, Smedby Ö, Kechagias S, Almer S, Lundberg P, Borga M, Leinhard OD. Consistent intensity inhomogeneity correction in water-fat MRI. J Magn Reson Imaging 2015;42:468-76.
  41. Ogawa M, Lester R, Akima H, Gorgey AS. Quantification of intermuscular and intramuscular adipose tissue using magnetic resonance imaging after neurodegenerative disorders. Neural Regen Res 2017;12:2100-5.
  42. Yoshiko A, Yamauchi K, Kato T, Ishida K, Koike T, Oshida Y, Akima H. Effects of post-fracture non-weight-bearing immobilization on muscle atrophy, intramuscular and intermuscular adipose tissues in the thigh and calf. Skeletal Radiol 2018;47:1541-9.
  43. Gorgey AS, Dudley GA. Skeletal muscle atrophy and increased intramuscular fat after incomplete spinal cord injury. Spinal Cord 2007;45:304-9.
  44. Hausman GJ, Basu U, Du M, Fernyhough-Culver M, Dodson MV. Intermuscular and intramuscular adipose tissues: Bad vs. good adipose tissues. Adipocyte 2014;3:242-55.
  45. Burakiewicz J, Sinclair CDJ, Fischer D, Walter GA, Kan HE, Hollingsworth KG. Quantifying fat replacement of muscle by quantitative MRI in muscular dystrophy. J Neurol 2017;264:2053-67.
  46. Dahlqvist JR, Vissing CR, Thomsen C, Vissing J. Severe paraspinal muscle involvement in facioscapulohumeral muscular dystrophy. Neurology 2014;83:1178-83.
  47. Wren TA, Bluml S, Tseng-Ong L, Gilsanz V. Three-point technique of fat quantification of muscle tissue as a marker of disease progression in Duchenne muscular dystrophy: preliminary study. AJR Am J Roentgenol 2008;190:W8-12.
  48. Pokharel SS, Macura KJ, Kamel IR, Zaheer A. Current MR imaging lipid detection techniques for diagnosis of lesions in the abdomen and pelvis. Radiographics 2013;33:681-702.
  49. Bley TA, Wieben O, François CJ, Brittain JH, Reeder SB. Fat and water magnetic resonance imaging. J Magn Reson Imaging 2010;31:4-18.
  50. Ünal E, Karaosmanoğlu AD, Akata D, Özmen MN, Karçaaltıncaba M. Invisible fat on CT: making it visible by MRI. Diagn Interv Radiol 2016;22:133-40.
  51. Earls JP, Krinsky GA. Abdominal and pelvic applications of opposed-phase MR imaging. AJR Am J Roentgenol 1997;169:1071-7.
  52. Basaran C, Karcaaltincaba M, Akata D, Karabulut N, Akinci D, Ozmen M, Akhan O. Fat-containing lesions of the liver: cross-sectional imaging findings with emphasis on MRI. AJR Am J Roentgenol 2005;184:1103-10.
  53. Li SR, Pui MH, Guo Y, Wang HJ, Guan J, Zhang XL, Pan WB. Efficacy of 3D VIBE Dixon fat quantification for differentiating clear-cell from non-clear-cell renal cell carcinoma. Clin Radiol 2018;73:975-80.
  54. Partridge SC, Singer L, Sun R, Wilmes LJ, Klifa CS, Lehman CD, Hylton NM. Diffusion-weighted MRI: influence of intravoxel fat signal and breast density on breast tumor conspicuity and apparent diffusion coefficient measurements. Magn Reson Imaging 2011;29:1215-21.
  55. Boutin RD, Yao L, Canter RJ, Lenchik L. Sarcopenia: Current Concepts and Imaging Implications. AJR Am J Roentgenol 2015;205:W255-66.
  56. Ermetici F, Briganti S, Delnevo A, Cannaò P, Leo GD, Benedini S, Terruzzi I, Sardanelli F, Luzi L. Bone marrow fat contributes to insulin sensitivity and adiponectin secretion in premenopausal women. Endocrine 2018;59:410-8.
  57. Messina C, Maffi G, Vitale JA, Ulivieri FM, Guglielmi G, Sconfienza LM. Diagnostic imaging of osteoporosis and sarcopenia: a narrative review. Quant Imaging Med Surg 2018;8:86-99.
  58. Kälin PS, Huber FA, Hamie QM, Issler LS, Farshad- Amacker NA, Ulbrich EJ, Guggenberger R. Quantitative MRI of Visually Intact Rotator Cuff Muscles by Multiecho Dixon-Based Fat Quantification and Diffusion Tensor Imaging. J Magn Reson Imaging 2019;49:109-17.
  59. Klupp E, Cervantes B, Schlaeger S, Inhuber S, Kreuzpointer F, Schwirtz A, Rohrmeier A, Dieckmeyer M, Hedderich DM, Diefenbach MN, Freitag F, Rummeny EJ, Zimmer C, Kirschke JS, Karampinos DC, Baum T. Paraspinal Muscle DTI Metrics Predict Muscle Strength. J Magn Reson Imaging 2019;50:816-23.
  60. Traussnigg S, Kienbacher C, Gajdošík M, Valkovič L, Halilbasic E, Stift J, Rechling C, Hofer H, Steindl-Munda P, Ferenci P, Wrba F, Trattnig S, Krššák M, Trauner M. Ultra-high-field magnetic resonance spectroscopy in non-alcoholic fatty liver disease: Novel mechanistic and diagnostic insights of energy metabolism in non- alcoholic steatohepatitis and advanced fibrosis. Liver Int 2017;37:1544-53.
  61. Wáng YXJ, Wang X, Wu P, Wang Y, Chen W, Chen H, Li J. Topics on quantitative liver magnetic resonance imaging. Quant Imaging Med Surg 2019;9:1840-90.
  62. Subhawong TK, Wang X, Durand DJ, Jacobs MA, Carrino JA, Machado AJ, Fayad LM. Proton MR spectroscopy in metabolic assessment of musculoskeletal lesions. AJR Am J Roentgenol 2012;198:162-72.
  63. Pan JW, Hamm JR, Hetherington HP, Rothman DL, Shulman RG. Correlation of lactate and pH in human skeletal muscle after exercise by 1H NMR. Magn Reson Med 1991;20:57-65.
  64. Fischer MA, Nanz D, Shimakawa A, Schirmer T, Guggenberger R, Chhabra A, Carrino JA, Andreisek G. Quantification of muscle fat in patients with low back pain: comparison of multi-echo MR imaging with single-voxel MR spectroscopy. Radiology 2013;266:555-63.
  65. Agten CA, Rosskopf AB, Gerber C, Pfirrmann CW. Quantification of early fatty infiltration of the rotator cuff muscles: comparison of multi-echo Dixon with single- voxel MR spectroscopy. Eur Radiol 2016;26:3719-27.
  66. © Quantitative Imaging in Medicine and Surgery. All rights reserved. Quant Imaging Med Surg 2020;10(8):1636-1649 | http://dx.doi.org/10.21037/qims.2020.02.06
  67. Aisen AM, Chenevert TL. MR spectroscopy: clinical perspective. Radiology 1989;173:593-9.
  68. Grimm A, Meyer H, Nickel MD, Nittka M, Raithel E, Chaudry O, Friedberger A, Uder M, Kemmler W, Engelke K, Quick HH. A Comparison between 6-point Dixon MRI and MR Spectroscopy to Quantify Muscle Fat in the Thigh of Subjects with Sarcopenia. J Frailty Aging 2019;8:21-6.
  69. Giannesini B, Izquierdo M, Le Fur Y, Cozzone PJ, Fingerle J, Himber J, Künnecke B, Von Kienlin M, Bendahan D. New experimental setup for studying strictly noninvasively skeletal muscle function in rat using 1H-magnetic resonance (MR) imaging and 31P-MR spectroscopy. Magn Reson Med 2005;54:1058-64.
  70. Valkovič L, Chmelík M, Krššák M. In-vivo 31P-MRS of skeletal muscle and liver: A way for non-invasive assessment of their metabolism. Anal Biochem 2017;529:193-215.
  71. Sedivy P, Dezortova M, Drobny M, Vlasakova Z, Herynek V, Hajek M. Differences in muscle metabolism in patients with type I diabetes -influence of gender and nephropathy studied by (31)P MR spectroscopy. Physiol Res 2018;67:433-41.
  72. Janssen BH, Voet NB, Nabuurs CI, Kan HE, de Rooy JW, Geurts AC, Padberg GW, van Engelen BG, Heerschap A. Distinct disease phases in muscles of facioscapulohumeral dystrophy patients identified by MR detected fat infiltration. PLoS One 2014;9:e85416.
  73. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics 2017;37:1483-503.
  74. Mannil M, Burgstaller JM, Thanabalasingam A, Winklhofer S, Betz M, Held U, Guggenberger R. Texture analysis of paraspinal musculature in MRI of the lumbar spine: analysis of the lumbar stenosis outcome study (LSOS) data. Skeletal Radiol 2018;47:947-54.
  75. Mannil M, Burgstaller JM, Held U, Farshad M, Guggenberger R. Correlation of texture analysis of paraspinal musculature on MRI with different clinical endpoints: Lumbar Stenosis Outcome Study (LSOS). Eur Radiol 2019;29:22-30.
  76. Huang CWC, Tseng IJ, Yang SW, Lin YK, Chan WP. Lumbar muscle volume in postmenopausal women with osteoporotic compression fractures: quantitative measurement using MRI. Eur Radiol 2019;29:4999-5006.
  77. Morone M, Bali MA, Tunariu N, Messiou C, Blackledge M, Grazioli L, Koh DM. Whole-Body MRI: Current Applications in Oncology. AJR Am J Roentgenol 2017;209:W336-49.
  78. Pourhassan M, Glüer CC, Pick P, Tigges W, Müller MJ. Impact of weight loss-associated changes in detailed body composition as assessed by whole-body MRI on plasma insulin levels and homeostatis model assessment index. Eur J Clin Nutr 2017;71:212-8.
  79. Cameron J, McPhee JS, Jones DA, Degens H. Five- year longitudinal changes in thigh muscle mass of septuagenarian men and women assessed with DXA and MRI. Aging Clin Exp Res 2020;32:617-24.
  80. Doyle SL, Donohoe CL, Lysaght J, Reynolds JV. Visceral obesity, metabolic syndrome, insulin resistance and cancer. Proc Nutr Soc 2012;71:181-9.
  81. Neeland IJ, Ayers CR, Rohatgi AK, Turer AT, Berry JD, Das SR, Vega GL, Khera A, McGuire DK, Grundy SM, de Lemos JA. Associations of visceral and abdominal subcutaneous adipose tissue with markers of cardiac and metabolic risk in obese adults. Obesity (Silver Spring) 2013;21:E439-47.
  82. Lee JJ, Pedley A, Hoffmann U, Massaro JM, Fox CS. Association of Changes in Abdominal Fat Quantity and Quality With Incident Cardiovascular Disease Risk Factors. J Am Coll Cardiol 2016;68:1509-21.
  83. Bamberg F, Hetterich H, Rospleszcz S, Lorbeer R, Auweter SD, Schlett CL, Schafnitzel A, Bayerl C, Schindler A, Saam T, Müller-Peltzer K, Sommer W, Zitzelsberger T, Machann J, Ingrisch M, Selder S, Rathmann W, Heier M, Linkohr B, Meisinger C, Weber C, Ertl-Wagner B, Massberg S, Reiser MF, Peters A. Subclinical Disease Burden as Assessed by Whole-Body MRI in Subjects With Prediabetes, Subjects With Diabetes, and Normal Control Subjects From the General Population: The KORA-MRI Study. Diabetes 2017;66:158-69.
  84. Britton KA, Massaro JM, Murabito JM, Kreger BE, Hoffmann U, Fox CS. Body fat distribution, incident cardiovascular disease, cancer, and all-cause mortality. J Am Coll Cardiol 2013;62:921-5.
  85. Rooks DS, Laurent D, Praestgaard J, Rasmussen S, Bartlett M, Tankó LB. Effect of bimagrumab on thigh muscle volume and composition in men with casting-induced atrophy. J Cachexia Sarcopenia Muscle 2017;8:727-34.
  86. Bolan PJ, Arentsen L, Sueblinvong T, Zhang Y, Moeller S, Carter JS, Downs LS, Ghebre R, Yee D, Froelich J, Hui S. Water-fat MRI for assessing changes in bone marrow composition due to radiation and chemotherapy in gynecologic cancer patients. J Magn Reson Imaging 2013;38:1578-84.
  87. Linge J, Borga M, West J, Tuthill T, Miller MR, Dumitriu A, Thomas EL, Romu T, Tunón P, Bell JD, Dahlqvist Leinhard O. Body Composition Profiling in the UK Biobank Imaging Study. Obesity (Silver Spring) 2018;26:1785-95.
  88. Lenchik L, Lenoir KM, Tan J, Boutin RD, Callahan KE, Kritchevsky SB, Wells BJ. Opportunistic Measurement of Skeletal Muscle Size and Muscle Attenuation on Computed Tomography Predicts 1-Year Mortality in Medicare Patients. J Gerontol A Biol Sci Med Sci 2019;74:1063-9.
  89. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, Martin FC, Michel JP, Rolland Y, Schneider SM, Topinková E, Vandewoude M, Zamboni M, People EWGoSiO. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing 2010;39:412-23.
  90. Boyer L, Chouaïd C, Bastuji-Garin S, Marcos E, Margarit L, Le Corvoisier P, Vervoitte L, Hamidou L, Frih L, Audureau E, Covali-Noroc A, Andujar P, Saakashvili Z, Lino A, Ghaleh B, Hue S, Derumeaux G, Housset B, Dubois-Randé JL, Boczkowski J, Maitre B, Adnot S. Aging-related systemic manifestations in COPD patients and cigarette smokers. PLoS One 2015;10:e0121539.
  91. Linge J, Heymsfield SB, Dahlqvist Leinhard O. On the Definition of Sarcopenia in the Presence of Aging and Obesity-Initial Results from UK Biobank. J Gerontol A Biol Sci Med Sci 2020;75:1309-16.
  92. Ruan XY, Gallagher D, Harris T, Albu J, Heymsfield S, Kuznia P, Heshka S. Estimating whole body intermuscular adipose tissue from single cross-sectional magnetic resonance images. J Appl Physiol (1985) 2007;102:748-54.
  93. Boettcher M, Machann J, Stefan N, Thamer C, Häring HU, Claussen CD, Fritsche A, Schick F. Intermuscular adipose tissue (IMAT): association with other adipose tissue compartments and insulin sensitivity. J Magn Reson Imaging 2009;29:1340-5. 100. Schlaffke L, Rehmann R, Rohm M, Otto LAM, de Luca A, Burakiewicz J, Baligand C, Monte J, den Harder C, Hooijmans MT, Nederveen A, Schlaeger S, Weidlich D, Karampinos DC, Stouge A, Vaeggemose M, D'Angelo MG, Arrigoni F, Kan HE, Froeling M. Multi-center evaluation of stability and reproducibility of quantitative MRI measures in healthy calf muscles. NMR Biomed 2019;32:e4119. 101. Karlsson A, Rosander J, Romu T, Tallberg J, Grönqvist A, Borga M, Dahlqvist Leinhard O. Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. J Magn Reson Imaging 2015;41:1558-69. 102. Schweitzer L, Geisler C, Pourhassan M, Braun W, Glüer CC, Bosy-Westphal A, Müller MJ. What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults? Am J Clin Nutr 2015;102:58-65. 103. Shen W, Chen J, Gantz M, Velasquez G, Punyanitya M, Heymsfield SB. A single MRI slice does not accurately predict visceral and subcutaneous adipose tissue changes during weight loss. Obesity (Silver Spring) 2012;20:2458-63. 104. Thomas EL, Bell JD. Influence of undersampling on magnetic resonance imaging measurements of intra- abdominal adipose tissue. Int J Obes Relat Metab Disord 2003;27:211-8. 105. Berger MM, Reintam-Blaser A, Calder PC, Casaer M, Hiesmayr MJ, Mayer K, Montejo JC, Pichard C, Preiser JC, van Zanten ARH, Bischoff SC, Singer P. Monitoring nutrition in the ICU. Clin Nutr 2019;38:584-93.

Authors
Huber, F. A., Del Grande, F., Rizzo, S., Guglielmi, G., & Guggenberger, R.
Affiliations
  • University Hospital Zurich (CH)
  • Ospedale regionale di Lugano (CH)
  • University of Foggia (IT)
Corresponding author
Huber, Florian Alexander
ORCID
Venue
Quantitative Imaging in Medicine and Surgery
Publisher
AME Publishing Company
Published
2020-08
Volume
10
Issue
8
Pages
1636-1649
ISSN
  • 2223-4292 (ISSN-L)
  • 2223-4306
DOI
10.21037/qims.2020.02.06
PMC
PMC7378099
OpenAlex
W3010956142
Cited by
102
References
111
Citations by year
2026: 29 · 2025: 22 · 2024: 23 · 2023: 13 · 2022: 7 · 2021: 7 · 2020: 1
FWCI
0.89
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • Quantitative Imaging in Medicine and Surgery (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by-nc-nd)
  • Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by-nc-nd)
  • Zurich Open Repository and Archive (University of Zurich)
Topics
  • Health Sciences › Medicine › Physiology › Body Composition Measurement Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Health Sciences › Medicine › Physiology › Nutrition and Health in Aging
Keywords
body composition assessment, adipose tissue, muscle composition, magnetic resonance imaging, quantitative MRI, soft tissue imaging

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Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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