Osteologie 2020; 29(02): 132-142
DOI: 10.1055/a-1130-4462

Quantitative MR-Bildgebung zur Charakterisierung der Skelettmuskulatur

Quantitative MR imaging to characterize the skeletal muscles
Klaus Engelke
1   Medizinische Klinik 3, FAU Universität Erlangen-Nürnberg und Universitätsklinikum Erlangen
3   Institut für Medizinische Physik, FAU Universität Erlangen-Nürnberg
Oliver Chaudry
1   Medizinische Klinik 3, FAU Universität Erlangen-Nürnberg und Universitätsklinikum Erlangen
3   Institut für Medizinische Physik, FAU Universität Erlangen-Nürnberg
Armin Nagel
2   Radiologisches Institut, Universitätsklinikum Erlangen, Friedrich‐Alexander‐Universität Erlangen‐Nürnberg (FAU), Erlangen
3   Institut für Medizinische Physik, FAU Universität Erlangen-Nürnberg
› Author Affiliations


Dieser Beitrag gibt einen Überblick über Magnetresonanztomographie-basierte (MRT-basierte) Methoden zur Quantifizierung der Muskeldegeneration. Neben Muskelvolumen kann mit Dixon-Bildgebung insbesondere der prozentuale Fettgehalt bestimmt werden. Daneben gibt es Ansätze, frühe Anzeichen einer Degeneration über die Verteilung des Entzündungsgrades oder der Natriumkonzentration in der Muskulatur zu visualisieren und quantifizieren. Bis auf die Natrium-Bildgebung werden diese Methoden bei Muskelerkrankungen routinemäßig zur Diagnose und Verlaufskontrolle eingesetzt.

Im Bereich der Osteologie und Gerontologie wird zwar die Bedeutung der Muskel-Knochen-Einheit unter anderem für Frakturprädiktion und Gebrechlichkeit im Alter immer wieder betont, Degeneration der Muskulatur wird aber im Wesentlichen über extrinsische Parameter wie Muskelkraft und -funktion erfasst. Häufig benutzte intrinsische Parameter wie DXA Lean Mass oder Muskelvolumen, bestimmt mit CT oder MRT, korrelieren nur mäßig mit extrinsischen Parametern. Eine genauere Charakterisierung von Muskelqualität sollte dieses Manko aber beseitigen. Mit CT und MRT stehen entsprechende Methoden zur Verfügung, die jetzt aber in Studien zur altersassoziierten Muskeldegeneration, in Interventionsstudien und in Studien zur Frakturrisikoprognostik auch eingesetzt werden müssen.


This article provides an overview of magnetic resonance imaging (MRI) based methods for the quantification of muscle degeneration. In addition to muscle volume, Dixon imaging can be used to determine the percentage of fat. In addition, there are approaches to visualize and quantify early signs of degeneration via the distribution of the degree of inflammation or the sodium concentration of the muscle. Except for sodium imaging, these methods are routinely used for diagnosis and follow-up in muscle diseases.

In osteology and gerontology, the importance of the muscle-bone unit for fracture prediction and frailty in old age is routinely emphasized, but degeneration of the muscles is mainly recorded via extrinsic parameters such as muscle strength and function. Frequently used intrinsic parameters such as DXA lean mass or muscle volume determined with CT or MRI correlate only moderately with extrinsic parameters. However, a more precise characterization of muscle quality should remedy this shortcoming. Appropriate methods are available with CT and MRI, but these must now be used in studies on age-related muscle degeneration, in intervention studies and in studies on fracture risk assessment

Publication History

Received: 27 February 2020

Accepted: 12 March 2020

Article published online:
02 June 2020

© Georg Thieme Verlag KG
Stuttgart · New York

  • Literatur

  • 1 Burakiewicz J, Sinclair CDJ, Fischer D. et al. Quantifying fat replacement of muscle by quantitative MRI in muscular dystrophy. J Neurol 2017; 264: 2053-2067
  • 2 Ten Dam L, van der Kooi AJ, Verhamme C. et al. Muscle imaging in inherited and acquired muscle diseases. Eur J Neurol 2016; 23 (04) : 688-703
  • 3 Weber A, ed. Magnetic Resonance Imaging of Skeletal Musculature. Medical Radiology - Diagnostic Imaging. ed. Reiser M, Hricak H, and Knauth M. 2014. Springer; : Heidelberg:
  • 4 Carlier PG, Marty B, Scheidegger O. et al. Skeletal Muscle Quantitative Nuclear Magnetic Resonance Imaging and Spectroscopy as an Outcome Measure for Clinical Trials. J Neuromuscul Dis 2016; 3 (01) : 1-28
  • 5 Strijkers GJ, Araujo ECA, Azzabou N. et al. Exploration of New Contrasts, Targets, and MR Imaging and Spectroscopy Techniques for Neuromuscular Disease - A Workshop Report of Working Group 3 of the Biomedicine and Molecular Biosciences COST Action BM1304 MYO-MRI. J Neuromuscul Dis 2019; 6 (01) : 1-30
  • 6 Cohen S, Nathan JA, and Goldberg AL. Muscle wasting in disease: molecular mechanisms and promising therapies. Nat Rev Drug Discov 2015; 14 (01) : 58-74
  • 7 Jo E, Lee SR, Park BS. et al. Potential mechanisms underlying the role of chronic inflammation in age-related muscle wasting. Aging Clin Exp Res 2012; 24 (05) : 412-22
  • 8 Perandini LA, Chimin P, Lutkemeyer DDS. et al. Chronic inflammation in skeletal muscle impairs satellite cells function during regeneration: can physical exercise restore the satellite cell niche?. FEBS J 2018; 285 (11) : 1973-1984
  • 9 Perez-Baos S, Prieto-Potin I, Roman-Blas JA. et al. Mediators and Patterns of Muscle Loss in Chronic Systemic Inflammation. Front Physiol 2018; 9 : 409
  • 10 Wang X, You T, Yang R. et al. Muscle strength is associated with adipose tissue gene expression of inflammatory adipokines in postmenopausal women. Age Ageing 2010; 39 (05) : 656-9
  • 11 Herrmann M, Engelke K, Ebert R. et al. Interactions between muscle and bone – where physics meets biology. Biomolecules, 2020 . submitted.
  • 12 Westbury LD, Fuggle NR, Syddall HE. et al. Relationships Between Markers of Inflammation and Muscle Mass, Strength and Function: Findings from the Hertfordshire Cohort Study. Calcif Tissue Int 2018; 102: 287-295
  • 13 Lipina C, and Hundal HS. Lipid modulation of skeletal muscle mass and function. J Cachexia Sarcopenia Muscle 2017; 8: 190-201
  • 14 Tuttle LJ, Sinacore DR, and Mueller MJ. Intermuscular adipose tissue is muscle specific and associated with poor functional performance. J Aging Res 2012; 2012 : 172957
  • 15 Chang KV, Chen JD, Wu WT. et al. Association between Loss of Skeletal Muscle Mass and Mortality and Tumor Recurrence in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Liver Cancer 2018; 7: 90-103
  • 16 Di Monaco M, Vallero F, Di Monaco R. et al. Skeletal muscle mass, fat mass, and hip bone mineral density in elderly women with hip fracture. J Bone Miner Metab 2007; 25: 237-42
  • 17 Schafer AL, Vittinghoff E, Lang TF. et al. Fat infiltration of muscle, diabetes, and clinical fracture risk in older adults. J Clin Endocrinol Metab 2010; 95:: E368-72
  • 18 Balogun S, Winzenberg T, Wills K. et al. Prospective Associations of Low Muscle Mass and Function with 10-Year Falls Risk, Incident Fracture and Mortality in Community-Dwelling Older Adults. J Nutr Health Aging 2017; 21: 843-848
  • 19 McGregor RA, Cameron-Smith D, and Poppitt SD. It is not just muscle mass: a review of muscle quality, composition and metabolism during ageing as determinants of muscle function and mobility in later life. Longev Healthspan 2014; 3: 9
  • 20 Dey DK, Bosaeus I, Lissner L. et al. Changes in body composition and its relation to muscle strength in 75-year-old men and women: a 5-year prospective follow-up study of the NORA cohort in Goteborg, Sweden. Nutrition 2009; 25: 613-9
  • 21 Csapo R, and Alegre LM. Effects of resistance training with moderate vs heavy loads on muscle mass and strength in the elderly: A meta-analysis. Scand J Med Sci Sports 2016; 26: 995-1006
  • 22 Lai CC, Tu YK, Wang TG. et al. Effects of resistance training, endurance training and whole-body vibration on lean body mass, muscle strength and physical performance in older people: a systematic review and network meta-analysis. Age Ageing 2018; 47: 367-373
  • 23 Lixandrao ME, Ugrinowitsch C, Berton R. et al. Magnitude of Muscle Strength and Mass Adaptations Between High-Load Resistance Training Versus Low-Load Resistance Training Associated with Blood-Flow Restriction: A Systematic Review and Meta-Analysis. Sports Med 2018; 48: 361-378
  • 24 Loenneke JP, Buckner SL, Dankel SJ. et al. Exercise-Induced Changes in Muscle Size do not Contribute to Exercise-Induced Changes in Muscle Strength. Sports Med 2019; 49: 987-991
  • 25 Vikberg S, Sorlen N, Branden L. et al. Effects of Resistance Training on Functional Strength and Muscle Mass in 70-Year-Old Individuals With Pre-sarcopenia: A Randomized Controlled Trial. J Am Med Dir Assoc 2019; 20 : 28-34
  • 26 Schaap LA, Koster A, and Visser M. Adiposity, muscle mass, and muscle strength in relation to functional decline in older persons. Epidemiol Rev 2013; 35:: 51-65
  • 27 Franzon K, Zethelius B, Cederholm T. et al. The impact of muscle function, muscle mass and sarcopenia on independent ageing in very old Swedish men. BMC Geriatr 2019; 19: 153
  • 28 Goodpaster BH, Park SW, Harris TB. et al. The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J Gerontol A Biol Sci Med Sci 2006; 61: 1059-64
  • 29 Cruz-Jentoft AJ, Bahat G, Bauer J. et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing 2019; 48: 601
  • 30 Dankel SJ, Buckner SL, Jessee MB. et al. Correlations Do Not Show Cause and Effect: Not Even for Changes in Muscle Size and Strength. Sports Med 2018; 48: 1-6
  • 31 Loenneke JP, Dankel SJ, Bell ZW. et al. Is muscle growth a mechanism for increasing strength?. Med Hypotheses 2019; 125:: 51-56
  • 32 Proctor DN, Melton LJ, Khosla S. et al. Relative influence of physical activity, muscle mass and strength on bone density. Osteoporos Int 2000; 11: 944-52
  • 33 Taniguchi Y, Makizako H, Kiyama R. et al. The Association between Osteoporosis and Grip Strength and Skeletal Muscle Mass in Community-Dwelling Older Women. Int J Environ Res Public Health 2019 16.
  • 34 Borggreve AS, den Boer RB, van Boxel GI. et al. The Predictive Value of Low Muscle Mass as Measured on CT Scans for Postoperative Complications and Mortality in Gastric Cancer Patients: A Systematic Review and Meta-Analysis. J Clin Med 2020 9.
  • 35 Ansari E, Chargi N, van Gemert JTM. et al. Low skeletal muscle mass is a strong predictive factor for surgical complications and a prognostic factor in oral cancer patients undergoing mandibular reconstruction with a free fibula flap. Oral Oncol 2019; 101:: 104530
  • 36 Limpawattana P, Theerakulpisut D, Wirasorn K. et al. The impact of skeletal muscle mass on survival outcome in biliary tract cancer patients. PLoS One 2018; 13: e0204985
  • 37 Rier HN, Jager A, Sleijfer S. et al. The Prevalence and Prognostic Value of Low Muscle Mass in Cancer Patients: A Review of the Literature. Oncologist 2016; 21: 1396-1409
  • 38 Buckinx F, Landi F, Cesari M. et al. Pitfalls in the measurement of muscle mass: a need for a reference standard. J Cachexia Sarcopenia Muscle, 2018; 9: 269-278
  • 39 Engelke K, Grimm A, Mühlberg A. et al. Imaging techniques in sarcopenia. Osteologie 2017; 26: 18-24
  • 40 Engelke K, Museyko O, Wang L. et al. Quantitative analysis of skeletal muscle by computed tomography imaging-State of the art. J Orthop Translat 2018; 15:: 91-103
  • 41 Wronska A, and Kmiec Z. Structural and biochemical characteristics of various white adipose tissue depots. Acta Physiol (Oxf) 2012; 205: 194-208
  • 42 Hollingsworth KG, de Sousa PL, Straub V. et al. Towards harmonization of protocols for MRI outcome measures in skeletal muscle studies: consensus recommendations from two TREAT-NMD NMR workshops, 2 May 2010, Stockholm, Sweden, 1-2 October 2009, Paris, France. Neuromuscul Disord 2012; 22 Suppl 2: S54-67
  • 43 Karampinos DC, Baum T, Nardo L. et al. Characterization of the regional distribution of skeletal muscle adipose tissue in type 2 diabetes using chemical shift-based water / fat separation. J Magn Reson Imaging 2012; 35: 899-907
  • 44 Addison O, Marcus RL, Lastayo PC. et al. Intermuscular fat: a review of the consequences and causes. Int J Endocrinol 2014; 2014:: 309570
  • 45 Ogawa M, Lester R, Akima H. et al. Quantification of intermuscular and intramuscular adipose tissue using magnetic resonance imaging after neurodegenerative disorders. Neural Regen Res 2017; 12: 2100-2105
  • 46 Dixon WT. Simple proton spectroscopic imaging. Radiology 1984; 153: 189-94
  • 47 Grimm A, Meyer H, Nickel MD. et al. 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: 21-26
  • 48 Grimm A, Meyer H, Nickel MD. et al. Evaluation of 2-point, 3-point, and 6-point Dixon magnetic resonance imaging with flexible echo timing for muscle fat quantification. Eur J Radiol 2018; 103:: 57-64
  • 49 Lareau-Trudel E, Le Troter A, Ghattas B. et al. Muscle Quantitative MR Imaging and Clustering Analysis in Patients with Facioscapulohumeral Muscular Dystrophy Type 1. PLoS One 2015; 10: e0132717
  • 50 Orgiu S, Lafortuna CL, Rastelli F. et al. Automatic muscle and fat segmentation in the thigh from T1-Weighted MRI. J Magn Reson Imaging 2016; 43: 601-10
  • 51 Positano V, Christiansen T, Santarelli MF. et al. Accurate segmentation of subcutaneous and intermuscular adipose tissue from MR images of the thigh. J Magn Reson Imaging 2009; 29: 677-84
  • 52 Tan CW, Li K, Yan ZN. et al. Towards large-scale MR thigh image analysis via an integrated quantification framework. Neurocomputing 2017; 229:: 63-76
  • 53 Valentinitsch A, Karampinos DC, Alizai H. et al. Automated unsupervised multi-parametric classification of adipose tissue depots in skeletal muscle. J Magn Reson Imaging 2013; 37: 917-27
  • 54 Gadermayr M, Disch C, Muller M. et al. A comprehensive study on automated muscle segmentation for assessing fat infiltration in neuromuscular diseases. Magn Reson Imaging 2018; 48 : 20-26
  • 55 Hu HH, Chen J, and Shen W. Segmentation and quantification of adipose tissue by magnetic resonance imaging. MAGMA 2016; 29: 259-76
  • 56 Makrogiannis S, Fishbein KW, Moore AZ. et al. Image-Based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization. IEEE Trans Biomed Eng 2016; 63: 805-13
  • 57 Pons C, Borotikar B, Garetier M. et al. Quantifying skeletal muscle volume and shape in humans using MRI: A systematic review of validity and reliability. PLoS One 2018; 13: e0207847
  • 58 Damon BM, Li K, and Bryant ND. Magnetic resonance imaging of skeletal muscle disease. Handb Clin Neurol 2016; 136:: 827-42
  • 59 Torriani M, Townsend E, Thomas BJ. et al. Lower leg muscle involvement in Duchenne muscular dystrophy: an MR imaging and spectroscopy study. Skeletal Radiol 2012; 41: 437-45
  • 60 Willis TA, Hollingsworth KG, Coombs A. et al. Quantitative magnetic resonance imaging in limb-girdle muscular dystrophy 2I: a multinational cross-sectional study. PLoS One 2014; 9: e90377
  • 61 Delmonico MJ, Harris TB, Visser M. et al. Longitudinal study of muscle strength, quality, and adipose tissue infiltration. Am J Clin Nutr 2009; 90: 1579-85
  • 62 Crawford RJ, Filli L, Elliott JM. et al. Age- and Level-Dependence of Fatty Infiltration in Lumbar Paravertebral Muscles of Healthy Volunteers. AJNR Am J Neuroradiol 2016; 37: 742-8
  • 63 Schlaeger S, Inhuber S, Rohrmeier A. et al. Association of paraspinal muscle water-fat MRI-based measurements with isometric strength measurements. Eur Radiol 2019; 29: 599-608
  • 64 Inhuber S, Sollmann N, Schlaeger S. et al. Associations of thigh muscle fat infiltration with isometric strength measurements based on chemical shift encoding-based water-fat magnetic resonance imaging. Eur Radiol Exp 2019; 3 (01) : 45
  • 65 Grimm A, Nickel MD, Chaudry O. et al. Feasibility of Dixon magnetic resonance imaging to quantify effects of physical training on muscle composition-A pilot study in young and healthy men. Eur J Radiol 2019; 114:: 160-166
  • 66 Baudin PY, Marty B, Robert B. et al. Qualitative and quantitative evaluation of skeletal muscle fatty degenerative changes using whole-body Dixon nuclear magnetic resonance imaging for an important reduction of the acquisition time. Neuromuscul Disord 2015; 25: 758-63
  • 67 Lee YH, Lee HS, Lee HE. et al. Whole-Body Muscle MRI in Patients with Hyperkalemic Periodic Paralysis Carrying the SCN4A Mutation T704 M: Evidence for Chronic Progressive Myopathy with Selective Muscle Involvement. J Clin Neurol 2015; 11: 331-8
  • 68 Schlaeger S, Klupp E, Weidlich D. et al. T2-Weighted Dixon Turbo Spin Echo for Accelerated Simultaneous Grading of Whole-Body Skeletal Muscle Fat Infiltration and Edema in Patients With Neuromuscular Diseases. J Comput Assist Tomogr 2018; 42: 574-579
  • 69 Ulbrich EJ, Nanz D, Leinhard OD. et al. 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: 449-458
  • 70 de Certaines JD, Larcher T, Duda D. et al. Application of texture analysis to muscle MRI: 1 – What kind of information should be expected from texture analysis?. EPJ Nonlinear Biomedical Physics 2015; 3 : 3
  • 71 Muhlberg A, Museyko O, Bousson V. et al. Three-dimensional Distribution of Muscle and Adipose Tissue of the Thigh at CT: Association with Acute Hip Fracture. Radiology 2019; 290: 426-434
  • 72 Nygren AT, Karlsson M, Norman B. et al. Effect of glycogen loading on skeletal muscle cross-sectional area and T2 relaxation time. Acta Physiol Scand 2001; 173: 385-90
  • 73 Carlier PG. Global T2 versus water T2 in NMR imaging of fatty infiltrated muscles: different methodology, different information and different implications. Neuromuscul Disord 2014; 24: 390-2
  • 74 Gold GE, Han E, Stainsby J. et al. Musculoskeletal MRI at 3.0 T: relaxation times and image contrast. AJR Am J Roentgenol 2004; 183: 343-51
  • 75 Janiczek RL, Gambarota G, Sinclair CD. et al. Simultaneous T(2) and lipid quantitation using IDEAL-CPMG. Magn Reson Med 2011; 66: 1293-302
  • 76 Yao L., and Gai N. Fat-corrected T2 measurement as a marker of active muscle disease in inflammatory myopathy. AJR Am J Roentgenol 2012; 198: W475-81
  • 77 Marty B, Baudin PY, Reyngoudt H. et al. Simultaneous muscle water T2 and fat fraction mapping using transverse relaxometry with stimulated echo compensation. NMR Biomed 2016; 29: 431-43
  • 78 Yao L, Yip AL, Shrader JA. et al. Magnetic resonance measurement of muscle T2, fat-corrected T2 and fat fraction in the assessment of idiopathic inflammatory myopathies. Rheumatology (Oxford) 2016; 55: 441-9
  • 79 Barnard AM, Willcocks RJ, Finanger EL. et al. Skeletal muscle magnetic resonance biomarkers correlate with function and sentinel events in Duchenne muscular dystrophy. PLoS One 2018; 13: e0194283
  • 80 Barnard AM, Willcocks RJ, Triplett WT. et al. MR biomarkers predict clinical function in Duchenne muscular dystrophy. Neurology 2020
  • 81 Schlaeger S, Weidlich D, Klupp E. et al. Water T2 Mapping in Fatty Infiltrated Thigh Muscles of Patients With Neuromuscular Diseases Using a T2 -Prepared 3 D Turbo Spin Echo With SPAIR. J Magn Reson Imaging 2019
  • 82 Kim HK, Laor T, Horn PS. et al. T2 mapping in Duchenne muscular dystrophy: distribution of disease activity and correlation with clinical assessments. Radiology 2010; 255: 899-908
  • 83 Kinugasa R, Kawakami Y, and Fukunaga T. Mapping activation levels of skeletal muscle in healthy volunteers: an MRI study. J Magn Reson Imaging 2006; 24: 1420-5
  • 84 Psatha M, Wu Z, Gammie F. et al. Age-related changes in the effects of strength training on lower leg muscles in healthy individuals measured using MRI. BMJ Open Sport Exerc Med 2017; 3: e000249
  • 85 Keller S, Yamamura J, Sedlacik J. et al. Diffusion tensor imaging combined with T2 mapping to quantify changes in the skeletal muscle associated with training and endurance exercise in competitive triathletes. Eur Radiol 2020
  • 86 Ochi E, Tsuchiya Y, and Nosaka K. Differences in post-exercise T2 relaxation time changes between eccentric and concentric contractions of the elbow flexors. Eur J Appl Physiol 2016 116. 2145-2154.
  • 87 Huang YL, Zhou JL, Jiang YM. et al. Assessment of lumbar paraspinal muscle activation using fMRI BOLD imaging and T2 mapping. Quant Imaging Med Surg 2020; 10: 106-115
  • 88 Kellermann M, Heiss R, Swoboda B. et al. Intramuscular Perfusion Response in Delayed Onset Muscle Soreness (DOMS): A Quantitative Analysis with Contrast-Enhanced Ultrasound (CEUS). Int J Sports Med 2017; 38: 833-841
  • 89 Hatakenaka M, Ueda M, Ishigami K. et al. Effects of aging on muscle T2 relaxation time: difference between fast- and slow-twitch muscles. Invest Radiol 2001; 36: 692-8
  • 90 Charles JP, Moon CH, and Anderst W. Determining subject-specific lower-limb muscle architecture data for musculoskeletal models using diffusion tensor MRI. J Biomech Eng 2018
  • 91 Damon BM, Froeling M, Buck AK. et al. Skeletal muscle diffusion tensor-MRI fiber tracking: rationale, data acquisition and analysis methods, applications and future directions. NMR Biomed. 2017 30. (3)
  • 92 Longwei X. Clinical application of diffusion tensor magnetic resonance imaging in skeletal muscle. Muscles Ligaments Tendons J 2012; 2: 19-24
  • 93 Damon BM, Wadington MC, Hornberger JL. et al. Absolute and relative contributions of BOLD effects to the muscle functional MRI signal intensity time course: effect of exercise intensity. Magn Reson Med 2007; 58: 335-45
  • 94 Jacobi B, Bongartz G, Partovi S. et al. Skeletal muscle BOLD MRI: from underlying physiological concepts to its usefulness in clinical conditions. J Magn Reson Imaging 2012; 35: 1253-65
  • 95 Noseworthy MD, Bulte DP, and Alfonsi J. BOLD magnetic resonance imaging of skeletal muscle. Semin Musculoskelet Radiol 2003; 7: 307-15
  • 96 Partovi S, Karimi S, Jacobi B. et al. Clinical implications of skeletal muscle blood-oxygenation-level-dependent (BOLD) MRI. MAGMA 2012; 25: 251-61
  • 97 Nagel AM, Amarteifio E, Lehmann-Horn F. et al. 3 Tesla sodium inversion recovery magnetic resonance imaging allows for improved visualization of intracellular sodium content changes in muscular channelopathies. Invest Radiol 2011; 46: 759-66
  • 98 Kopp C, Linz P, Dahlmann A. et al. 23Na magnetic resonance imaging-determined tissue sodium in healthy subjects and hypertensive patients. Hypertension 2013; 61: 635-40
  • 99 Gerhalter T, Gast LV, Marty B. et al. (23) Na MRI depicts early changes in ion homeostasis in skeletal muscle tissue of patients with duchenne muscular dystrophy. J Magn Reson Imaging 2019; 50: 1103-1113
  • 100 Schoenau E. From mechanostat theory to development of the “Functional Muscle-Bone-Unit”. J Musculoskelet Neuronal Interact 2005; 5: 232-8
  • 101 Burr DB. Muscle strength, bone mass, and age-related bone loss. J Bone Miner Res 1997; 12: 1547-51
  • 102 Kaji H. Interaction between Muscle and Bone. J Bone Metab 2014; 21: 29-40
  • 103 Takamori M, Akiyama S, Yoshida K. et al. T2 Distribution in the Forearm Muscles and the T2 Threshold for Defining Activated Muscle. Magn Reson Med Sci 2019; 18: 184-193