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Semin Musculoskelet Radiol 2024; 28(05): 576-593
DOI: 10.1055/s-0044-1788693
Review Article

Magnetic Resonance Assessment of Bone Quality in Metabolic Bone Diseases

Nico Sollmann 
1   Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
2   Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
3   TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
,
Michael Dieckmeyer
2   Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
4   Department of Diagnostic, Interventional, and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland
,
Julio Carballido-Gamio
5   Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
,
Anh Tu Van
6   Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
,
Dimitrios C. Karampinos
6   Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
,
Georg C. Feuerriegel
6   Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
7   Department of Radiology, Balgrist University Hospital, Faculty of Medicine, University of Zurich, Zurich, Switzerland
,
Sarah C. Foreman
6   Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
,
Alexandra S. Gersing
6   Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
8   Department of Neuroradiology, LMU University Hospital, LMU Munich, Munich, Germany
,
Roland Krug
9   Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California
,
Thomas Baum
2   Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
,
Jan S. Kirschke
2   Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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Abstract

Metabolic bone diseases (MBDs) are a diverse group of diseases, affecting the mass or structure of bones and leading to reduced bone quality. Parameters representing different aspects of bone health can be obtained from various magnetic resonance imaging (MRI) methods such as proton MR spectroscopy, as well as chemical shift encoding-based water-fat imaging, that have been frequently applied to study bone marrow in particular. Furthermore, T2* mapping and high-resolution trabecular bone imaging have been implemented to study bone microstructure. In addition, quantitative susceptibility mapping and ultrashort echo time imaging are used for trabecular and cortical bone assessment. This review offers an overview of technical aspects, as well as major clinical applications and derived main findings, for MRI-based assessment of bone quality in MBDs. It focuses on osteoporosis as the most common MBD.

Keywords

chemical shift imaging - fat fraction - femur - osteoporosis - spine


Publication History

Article published online:
15 October 2024

© 2024. Thieme. All rights reserved.

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  • References

  • 1 El Demellawy D, Davila J, Shaw A, Nasr Y. Brief review on metabolic bone disease. Acad Forensic Pathol 2018; 8 (03) 611-640
    CrossrefPubMedSearch in Google Scholar
  • 2 Sobh MM, Abdalbary M, Elnagar S. et al. Secondary osteoporosis and metabolic bone diseases. J Clin Med 2022; 11 (09) 11
    CrossrefPubMedSearch in Google Scholar
  • 3 Horvai AE, Boyce BF. Metabolic bone diseases. Semin Diagn Pathol 2011; 28 (01) 13-25
    CrossrefPubMedSearch in Google Scholar
  • 4 Charoenngam N, Nasr A, Shirvani A, Holick MF. Hereditary metabolic bone diseases: a review of pathogenesis, diagnosis and management. Genes (Basel) 2022; 13 (10) 13
    CrossrefPubMedSearch in Google Scholar
  • 5 Chang CY, Rosenthal DI, Mitchell DM, Handa A, Kattapuram SV, Huang AJ. Imaging findings of metabolic bone disease. Radiographics 2016; 36 (06) 1871-1887
    CrossrefPubMedSearch in Google Scholar
  • 6 Haugeberg G. Imaging of metabolic bone diseases. Best Pract Res Clin Rheumatol 2008; 22 (06) 1127-1139
    CrossrefPubMedSearch in Google Scholar
  • 7 Cooper C, Campion G, Melton III LJ. Hip fractures in the elderly: a world-wide projection. Osteoporos Int 1992; 2 (06) 285-289
    CrossrefPubMedSearch in Google Scholar
  • 8 Compston JE, McClung MR, Leslie WD. Osteoporosis. Lancet 2019; 393 (10169): 364-376
    CrossrefPubMedSearch in Google Scholar
  • 9 NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy. Osteoporosis prevention, diagnosis, and therapy. JAMA 2001; 285 (06) 785-795
    CrossrefPubMedSearch in Google Scholar
  • 10 Hallberg I, Bachrach-Lindström M, Hammerby S, Toss G, Ek AC. Health-related quality of life after vertebral or hip fracture: a seven-year follow-up study. BMC Musculoskelet Disord 2009; 10: 135
    CrossrefPubMedSearch in Google Scholar
  • 11 Tarride JE, Burke N, Leslie WD. et al. Loss of health related quality of life following low-trauma fractures in the elderly. BMC Geriatr 2016; 16: 84
    CrossrefPubMedSearch in Google Scholar
  • 12 Bliuc D, Nguyen ND, Milch VE, Nguyen TV, Eisman JA, Center JR. Mortality risk associated with low-trauma osteoporotic fracture and subsequent fracture in men and women. JAMA 2009; 301 (05) 513-521
    CrossrefPubMedSearch in Google Scholar
  • 13 Bliuc D, Nguyen ND, Nguyen TV, Eisman JA, Center JR. Compound risk of high mortality following osteoporotic fracture and refracture in elderly women and men. J Bone Miner Res 2013; 28 (11) 2317-2324
    CrossrefPubMedSearch in Google Scholar
  • 14 Center JR, Nguyen TV, Schneider D, Sambrook PN, Eisman JA. Mortality after all major types of osteoporotic fracture in men and women: an observational study. Lancet 1999; 353 (9156): 878-882
    CrossrefPubMedSearch in Google Scholar
  • 15 Melton III LJ, Kallmes DF. Epidemiology of vertebral fractures: implications for vertebral augmentation. Acad Radiol 2006; 13 (05) 538-545
    CrossrefPubMedSearch in Google Scholar
  • 16 Nevitt MC, Ettinger B, Black DM. et al. The association of radiographically detected vertebral fractures with back pain and function: a prospective study. Ann Intern Med 1998; 128 (10) 793-800
    CrossrefPubMedSearch in Google Scholar
  • 17 Melton III LJ, Atkinson EJ, Cooper C, O'Fallon WM, Riggs BL. Vertebral fractures predict subsequent fractures. Osteoporos Int 1999; 10 (03) 214-221
    CrossrefPubMedSearch in Google Scholar
  • 18 Kanis JA. Diagnosis of osteoporosis and assessment of fracture risk. Lancet 2002; 359 (9321): 1929-1936
    CrossrefPubMedSearch in Google Scholar
  • 19 Kanis JA. Diagnosis of osteoporosis. Osteoporos Int 1997; 7 (Suppl. 03) S108-S116
    CrossrefPubMedSearch in Google Scholar
  • 20 Link TM, Kazakia G. Update on imaging-based measurement of bone mineral density and quality. Curr Rheumatol Rep 2020; 22 (05) 13
    CrossrefPubMedSearch in Google Scholar
  • 21 Löffler MT, Sollmann N, Mei K. et al. X-ray-based quantitative osteoporosis imaging at the spine. Osteoporos Int 2020; 31 (02) 233-250
    CrossrefPubMedSearch in Google Scholar
  • 22 Praveen AD, Sollmann N, Baum T, Ferguson SJ, Benedikt H. CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis. Osteoporos Int 2024; 35 (06) 971-996
    CrossrefPubMedSearch in Google Scholar
  • 23 Sollmann N, Kirschke JS, Kronthaler S. et al. Imaging of the osteoporotic spine: quantitative approaches in diagnostics and for the prediction of the individual fracture risk. Röfo Fortschr Geb Röntgenstr Neuen Bildgeb Verfahr 2022; 194 (10) 1088-1099
    Thieme ConnectPubMedSearch in Google Scholar
  • 24 Ammann P, Rizzoli R. Bone strength and its determinants. Osteoporos Int 2003; 14 (Suppl. 03) S13-S18
    CrossrefPubMedSearch in Google Scholar
  • 25 Schuit SC, van der Klift M, Weel AE. et al. Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone 2004; 34 (01) 195-202
    CrossrefPubMedSearch in Google Scholar
  • 26 Sollmann N, Löffler MT, Kronthaler S. et al. MRI-based quantitative osteoporosis imaging at the spine and femur. j Magn Reson Imaging 2021; 54 (01) 12-35
    CrossrefPubMedSearch in Google Scholar
  • 27 Karampinos DC, Ruschke S, Dieckmeyer M. et al. Quantitative MRI and spectroscopy of bone marrow. J Magn Reson Imaging 2018; 47 (02) 332-353
    CrossrefPubMedSearch in Google Scholar
  • 28 Majumdar S. Magnetic resonance imaging of trabecular bone structure. Top Magn Reson Imaging 2002; 13 (05) 323-334
    CrossrefPubMedSearch in Google Scholar
  • 29 Chang G, Boone S, Martel D. et al. MRI assessment of bone structure and microarchitecture. J Magn Reson Imaging 2017; 46 (02) 323-337
    CrossrefPubMedSearch in Google Scholar
  • 30 Fazeli PK, Horowitz MC, MacDougald OA. et al. Marrow fat and bone—new perspectives. J Clin Endocrinol Metab 2013; 98 (03) 935-945
    CrossrefPubMedSearch in Google Scholar
  • 31 Rosen CJ, Bouxsein ML. Mechanisms of disease: is osteoporosis the obesity of bone?. Nat Clin Pract Rheumatol 2006; 2 (01) 35-43
    CrossrefPubMedSearch in Google Scholar
  • 32 Kugel H, Jung C, Schulte O, Heindel W. Age- and sex-specific differences in the 1H-spectrum of vertebral bone marrow. J Magn Reson Imaging 2001; 13 (02) 263-268
    CrossrefPubMedSearch in Google Scholar
  • 33 Griffith JF, Yeung DK, Antonio GE. et al. Vertebral bone mineral density, marrow perfusion, and fat content in healthy men and men with osteoporosis: dynamic contrast-enhanced MR imaging and MR spectroscopy. Radiology 2005; 236 (03) 945-951
    CrossrefPubMedSearch in Google Scholar
  • 34 Kühn JP, Hernando D, Meffert PJ. et al. Proton-density fat fraction and simultaneous R2* estimation as an MRI tool for assessment of osteoporosis. Eur Radiol 2013; 23 (12) 3432-3439
    CrossrefPubMedSearch in Google Scholar
  • 35 Shen W, Gong X, Weiss J, Jin Y. Comparison among T1-weighted magnetic resonance imaging, modified Dixon method, and magnetic resonance spectroscopy in measuring bone marrow fat. J Obes 2013; 2013: 298675
    CrossrefPubMedSearch in Google Scholar
  • 36 Wehrli FW, Ford JC, Attie M, Kressel HY, Kaplan FS. Trabecular structure: preliminary application of MR interferometry. Radiology 1991; 179 (03) 615-621
    CrossrefPubMedSearch in Google Scholar
  • 37 Wehrli FW, Song HK, Saha PK, Wright AC. Quantitative MRI for the assessment of bone structure and function. NMR Biomed 2006; 19 (07) 731-764
    CrossrefPubMedSearch in Google Scholar
  • 38 Majumdar S, Thomasson D, Shimakawa A, Genant HK. Quantitation of the susceptibility difference between trabecular bone and bone marrow: experimental studies. Magn Reson Med 1991; 22 (01) 111-127
    CrossrefPubMedSearch in Google Scholar
  • 39 Reichert IL, Robson MD, Gatehouse PD. et al. Magnetic resonance imaging of cortical bone with ultrashort TE pulse sequences. Magn Reson Imaging 2005; 23 (05) 611-618
    CrossrefPubMedSearch in Google Scholar
  • 40 Wang Y, Liu T. Quantitative susceptibility mapping (QSM): decoding MRI data for a tissue magnetic biomarker. Magn Reson Med 2015; 73 (01) 82-101
    CrossrefPubMedSearch in Google Scholar
  • 41 Dimov AV, Liu Z, Spincemaille P, Prince MR, Du J, Wang Y. Bone quantitative susceptibility mapping using a chemical species-specific R2* signal model with ultrashort and conventional echo data. Magn Reson Med 2018; 79 (01) 121-128
    CrossrefPubMedSearch in Google Scholar
  • 42 Li Z, Rosen CJ. The multifaceted roles of bone marrow adipocytes in bone and hematopoietic homeostasis. J Clin Endocrinol Metab 2023; 108 (12) e1465-e1472
    CrossrefPubMedSearch in Google Scholar
  • 43 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 (05) 1011-1014
    CrossrefPubMedSearch in Google Scholar
  • 44 Dieckmeyer M, Ruschke S, Cordes C. et al. The need for T2 correction on MRS-based vertebral bone marrow fat quantification: implications for bone marrow fat fraction age dependence. NMR Biomed 2015; 28 (04) 432-439
    CrossrefPubMedSearch in Google Scholar
  • 45 Li X, Kuo D, Schafer AL. et al. Quantification of vertebral bone marrow fat content using 3.  . Tesla MR spectroscopy: reproducibility, vertebral variation, and applications in osteoporosis. J Magn Reson Imaging 2011; 33 (04) 974-979
    CrossrefPubMedSearch in Google Scholar
  • 46 Yeung DK, Griffith JF, Antonio GE, Lee FK, Woo J, Leung PC. Osteoporosis is associated with increased marrow fat content and decreased marrow fat unsaturation: a proton MR spectroscopy study. J Magn Reson Imaging 2005; 22 (02) 279-285
    CrossrefPubMedSearch in Google Scholar
  • 47 Griffith JF, Yeung DK, Antonio GE. et al. Vertebral marrow fat content and diffusion and perfusion indexes in women with varying bone density: MR evaluation. Radiology 2006; 241 (03) 831-838
    CrossrefPubMedSearch in Google Scholar
  • 48 Griffith JF, Yeung DK, Tsang PH. et al. Compromised bone marrow perfusion in osteoporosis. J Bone Miner Res 2008; 23 (07) 1068-1075
    CrossrefPubMedSearch in Google Scholar
  • 49 Baum T, Yap SP, Karampinos DC. et al. Does vertebral bone marrow fat content correlate with abdominal adipose tissue, lumbar spine bone mineral density, and blood biomarkers in women with type 2 diabetes mellitus?. J Magn Reson Imaging 2012; 35 (01) 117-124
    CrossrefPubMedSearch in Google Scholar
  • 50 Manenti G, Capuani S, Fusco A, Fanucci E, Tarantino U, Simonetti G. Osteoporosis detection by 3T diffusion tensor imaging and MRI spectroscopy in women older than 60 years. Aging Clin Exp Res 2013; 25 (Suppl. 01) S31-S34
    CrossrefPubMedSearch in Google Scholar
  • 51 Manenti G, Capuani S, Fanucci E. et al. Diffusion tensor imaging and magnetic resonance spectroscopy assessment of cancellous bone quality in femoral neck of healthy, osteopenic and osteoporotic subjects at 3T: preliminary experience. Bone 2013; 55 (01) 7-15
    CrossrefPubMedSearch in Google Scholar
  • 52 Patsch JM, Li X, Baum T. et al. Bone marrow fat composition as a novel imaging biomarker in postmenopausal women with prevalent fragility fractures. J Bone Miner Res 2013; 28 (08) 1721-1728
    CrossrefPubMedSearch in Google Scholar
  • 53 Schwartz AV, Sigurdsson S, Hue TF. et al. Vertebral bone marrow fat associated with lower trabecular BMD and prevalent vertebral fracture in older adults. J Clin Endocrinol Metab 2013; 98 (06) 2294-2300
    CrossrefPubMedSearch in Google Scholar
  • 54 Cohen A, Shen W, Dempster DW. et al. Marrow adiposity assessed on transiliac crest biopsy samples correlates with noninvasive measurement of marrow adiposity by proton magnetic resonance spectroscopy ((1)H-MRS) at the spine but not the femur. Osteoporos Int 2015; 26 (10) 2471-2478
    CrossrefPubMedSearch in Google Scholar
  • 55 Karampinos DC, Ruschke S, Gordijenko O. et al. Association of MRS-based vertebral bone marrow fat fraction with bone strength in a human in vitro model. J Osteoporos 2015; 2015: 152349
    CrossrefPubMedSearch in Google Scholar
  • 56 Di Pietro G, Capuani S, Manenti G. et al. Bone marrow lipid profiles from peripheral skeleton as potential biomarkers for osteoporosis: a 1H-MR spectroscopy study. Acad Radiol 2016; 23 (03) 273-283
    CrossrefPubMedSearch in Google Scholar
  • 57 Sheu Y, Amati F, Schwartz AV. et al; Osteoporotic Fractures in Men (MrOS) Research Group. Vertebral bone marrow fat, bone mineral density and diabetes: the Osteoporotic Fractures in Men (MrOS) study. Bone 2017; 97: 299-305
    CrossrefPubMedSearch in Google Scholar
  • 58 Mistry SD, Woods GN, Sigurdsson S. et al. Sex hormones are negatively associated with vertebral bone marrow fat. Bone 2018; 108: 20-24
    CrossrefPubMedSearch in Google Scholar
  • 59 He J, Fang H, Li X. Vertebral bone marrow fat content in normal adults with varying bone densities at 3T magnetic resonance imaging. Acta Radiol 2019; 60 (04) 509-515
    CrossrefPubMedSearch in Google Scholar
  • 60 Borelli C, Vergara D, Guglielmi R, Aucella F, Testini V, Guglielmi G. Assessment of bone marrow fat by 3-Tesla magnetic resonance spectroscopy in patients with chronic kidney disease. Quant Imaging Med Surg 2023; 13 (11) 7432-7443
    CrossrefPubMedSearch in Google Scholar
  • 61 El Ghoch M, Calugi S, Lamburghini S, Dalle Grave R. Anorexia nervosa and body fat distribution: a systematic review. Nutrients 2014; 6 (09) 3895-3912
    CrossrefPubMedSearch in Google Scholar
  • 62 Hussain AA, Hübel C, Hindborg M. et al. Increased lipid and lipoprotein concentrations in anorexia nervosa: a systematic review and meta-analysis. Int J Eat Disord 2019; 52 (06) 611-629
    CrossrefPubMedSearch in Google Scholar
  • 63 Ilyas A, Hübel C, Stahl D. et al. The metabolic underpinning of eating disorders: a systematic review and meta-analysis of insulin sensitivity. Mol Cell Endocrinol 2019; 497: 110307
    CrossrefPubMedSearch in Google Scholar
  • 64 Bredella MA, Fazeli PK, Miller KK. et al. Increased bone marrow fat in anorexia nervosa. J Clin Endocrinol Metab 2009; 94 (06) 2129-2136
    CrossrefPubMedSearch in Google Scholar
  • 65 Fazeli PK, Bredella MA, Freedman L. et al. Marrow fat and preadipocyte factor-1 levels decrease with recovery in women with anorexia nervosa. J Bone Miner Res 2012; 27 (09) 1864-1871
    CrossrefPubMedSearch in Google Scholar
  • 66 Singhal V, Bose A, Liang Y. et al. Marrow adipose tissue in adolescent girls with obesity. Bone 2019; 129: 115103
    CrossrefPubMedSearch in Google Scholar
  • 67 Mourad C, Cosentino A, Nicod Lalonde M, Omoumi P. Advances in bone marrow imaging: strengths and limitations from a clinical perspective. Semin Musculoskelet Radiol 2023; 27 (01) 3-21
    Thieme ConnectPubMedSearch in Google Scholar
  • 68 Bydder M, Yokoo T, Hamilton G. et al. Relaxation effects in the quantification of fat using gradient echo imaging. Magn Reson Imaging 2008; 26 (03) 347-359
    CrossrefPubMedSearch in Google Scholar
  • 69 Wang X, Hernando D, Reeder SB. Sensitivity of chemical shift-encoded fat quantification to calibration of fat MR spectrum. Magn Reson Med 2016; 75 (02) 845-851
    CrossrefPubMedSearch in Google Scholar
  • 70 Hernando D, Liang ZP, Kellman P. Chemical shift-based water/fat separation: a comparison of signal models. Magn Reson Med 2010; 64 (03) 811-822
    CrossrefPubMedSearch in Google Scholar
  • 71 Colgan TJ, Hernando D, Sharma SD, Reeder SB. The effects of concomitant gradients on chemical shift encoded MRI. Magn Reson Med 2017; 78 (02) 730-738
    CrossrefPubMedSearch in Google Scholar
  • 72 Liu CY, McKenzie CA, Yu H, Brittain JH, Reeder SB. Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise. Magn Reson Med 2007; 58 (02) 354-364
    CrossrefPubMedSearch in Google Scholar
  • 73 Ruschke S, Eggers H, Kooijman H. et al. Correction of phase errors in quantitative water-fat imaging using a monopolar time-interleaved multi-echo gradient echo sequence. Magn Reson Med 2017; 78 (03) 984-996
    CrossrefPubMedSearch in Google Scholar
  • 74 Karampinos DC, Ruschke S, Dieckmeyer M. et al. Modeling of T2* decay in vertebral bone marrow fat quantification. NMR Biomed 2015; 28 (11) 1535-1542
    CrossrefPubMedSearch in Google Scholar
  • 75 Li G, Xu Z, Gu H. et al. Comparison of chemical shift-encoded water-fat MRI and MR spectroscopy in quantification of marrow fat in postmenopausal females. J Magn Reson Imaging 2017; 45 (01) 66-73
    CrossrefPubMedSearch in Google Scholar
  • 76 Ruschke S, Pokorney A, Baum T. et al. Measurement of vertebral bone marrow proton density fat fraction in children using quantitative water-fat MRI. Magn Reson Mater Biol Phys Med 2017; 30 (05) 449-460
    CrossrefPubMedSearch in Google Scholar
  • 77 Martel D, Leporq B, Saxena A. et al. 3T chemical shift-encoded MRI: detection of altered proximal femur marrow adipose tissue composition in glucocorticoid users and validation with magnetic resonance spectroscopy. J Magn Reson Imaging 2019; 50 (02) 490-496
    CrossrefPubMedSearch in Google Scholar
  • 78 Baum T, Yap SP, Dieckmeyer M. et al. Assessment of whole spine vertebral bone marrow fat using chemical shift-encoding based water-fat MRI. J Magn Reson Imaging 2015; 42 (04) 1018-1023
    CrossrefPubMedSearch in Google Scholar
  • 79 Moorthi RN, Fadel W, Eckert GJ, Ponsler-Sipes K, Moe SM, Lin C. Bone marrow fat is increased in chronic kidney disease by magnetic resonance spectroscopy. Osteoporos Int 2015; 26 (06) 1801-1807
    CrossrefPubMedSearch in Google Scholar
  • 80 Paccou J, Badr S, Lombardo D. et al. Bone marrow adiposity and fragility fractures in postmenopausal women: the ADIMOS case-control study. J Clin Endocrinol Metab 2023; 108 (10) 2526-2536
    CrossrefPubMedSearch in Google Scholar
  • 81 Baum T, Rohrmeier A, Syväri J. et al. Anatomical variation of age-related changes in vertebral bone marrow composition using chemical shift encoding-based water-fat magnetic resonance imaging. Front Endocrinol (Lausanne) 2018; 9: 141
    CrossrefPubMedSearch in Google Scholar
  • 82 Schmeel FC, Luetkens JA, Enkirch SJ. et al. Proton density fat fraction (PDFF) MR imaging for differentiation of acute benign and neoplastic compression fractures of the spine. Eur Radiol 2018; 28 (12) 5001-5009
    CrossrefPubMedSearch in Google Scholar
  • 83 Schmeel FC, Luetkens JA, Wagenhäuser PJ. et al. Proton density fat fraction (PDFF) MRI for differentiation of benign and malignant vertebral lesions. Eur Radiol 2018; 28 (06) 2397-2405
    CrossrefPubMedSearch in Google Scholar
  • 84 Zhao Y, Huang M, Ding J. et al. Prediction of abnormal bone density and osteoporosis from lumbar spine MR using modified Dixon Quant in 257 subjects with quantitative computed tomography as reference. J Magn Reson Imaging 2019; 49 (02) 390-399
    CrossrefPubMedSearch in Google Scholar
  • 85 Beekman KM, Regenboog M, Nederveen AJ. et al. Gender- and age-associated differences in bone marrow adipose tissue and bone marrow fat unsaturation throughout the skeleton, quantified using chemical shift encoding-based water-fat MRI. Front Endocrinol (Lausanne) 2022; 13: 815835
    CrossrefPubMedSearch in Google Scholar
  • 86 Gassert FT, Kufner A, Gassert FG. et al. MR-based proton density fat fraction (PDFF) of the vertebral bone marrow differentiates between patients with and without osteoporotic vertebral fractures. Osteoporos Int 2022; 33 (02) 487-496
    CrossrefPubMedSearch in Google Scholar
  • 87 Leonhardt Y, Ketschau J, Ruschke S. et al. Associations of incidental vertebral fractures and longitudinal changes of MR-based proton density fat fraction and T2* measurements of vertebral bone marrow. Front Endocrinol (Lausanne) 2022; 13: 1046547
    CrossrefPubMedSearch in Google Scholar
  • 88 Badr S, Cotten A, Mentaverri R. et al. Relationship between bone marrow adipose tissue and kidney function in postmenopausal women. Bone Rep 2023; 19: 101713
    CrossrefPubMedSearch in Google Scholar
  • 89 Li X, Hu Y, Xie Y. et al. T2*-corrected Q-Dixon and reduced-FOV diffusion kurtosis imaging (DKI) parameters: correlation with QCT-derived bone mineral density (BMD) and ability to identify abnormal BMD and osteoporosis in postmenopausal women. Quant Imaging Med Surg 2023; 13 (07) 4130-4146
    CrossrefPubMedSearch in Google Scholar
  • 90 Liu Z, Huang D, Jiang Y, Ma X, Zhang Y, Chang R. Correlation of R2* with fat fraction and bone mineral density and its role in quantitative assessment of osteoporosis. Eur Radiol 2023; 33 (09) 6001-6008
    CrossrefPubMedSearch in Google Scholar
  • 91 Tang R, Tang G, Hua T, Tu Y, Ji R, Zhu J. mDIXON-Quant technique diagnostic accuracy for assessing bone mineral density in male adult population. BMC Musculoskelet Disord 2023; 24 (01) 125
    CrossrefPubMedSearch in Google Scholar
  • 92 Zhou F, Sheng B, Lv F. Quantitative analysis of vertebral fat fraction and R2* in osteoporosis using IDEAL-IQ sequence. BMC Musculoskelet Disord 2023; 24 (01) 721
    CrossrefPubMedSearch in Google Scholar
  • 93 Martel D, Leporq B, Bruno M, Regatte RR, Honig S, Chang G. Chemical shift-encoded MRI for assessment of bone marrow adipose tissue fat composition: pilot study in premenopausal versus postmenopausal women. Magn Reson Imaging 2018; 53: 148-155
    CrossrefPubMedSearch in Google Scholar
  • 94 Bayraktar HH, Morgan EF, Niebur GL, Morris GE, Wong EK, Keaveny TM. Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. J Biomech 2004; 37 (01) 27-35
    CrossrefPubMedSearch in Google Scholar
  • 95 Oftadeh R, Perez-Viloria M, Villa-Camacho JC, Vaziri A, Nazarian A. Biomechanics and mechanobiology of trabecular bone: a review. J Biomech Eng 2015; 137 (01) 0108021-01080215
    CrossrefPubMedSearch in Google Scholar
  • 96 Ruetten PPR, Gillard JH, Graves MJ. Introduction to quantitative susceptibility mapping and susceptibility weighted imaging. Br J Radiol 2019; 92 (1101): 20181016
    CrossrefPubMedSearch in Google Scholar
  • 97 Reichenbach JR, Schweser F, Serres B, Deistung A. Quantitative susceptibility mapping: concepts and applications. Clin Neuroradiol 2015; 25 (Suppl. 02) 225-230
    CrossrefPubMedSearch in Google Scholar
  • 98 Zhang X, Guo Y, Chen Y. et al. Reproducibility of quantitative susceptibility mapping in lumbar vertebra. Quant Imaging Med Surg 2019; 9 (04) 691-699
    CrossrefPubMedSearch in Google Scholar
  • 99 Chen Y, Guo Y, Zhang X, Mei Y, Feng Y, Zhang X. Bone susceptibility mapping with MRI is an alternative and reliable biomarker of osteoporosis in postmenopausal women. Eur Radiol 2018; 28 (12) 5027-5034
    CrossrefPubMedSearch in Google Scholar
  • 100 Guo Y, Chen Y, Zhang X. et al. Magnetic susceptibility and fat content in the lumbar spine of postmenopausal women with varying bone mineral density. J Magn Reson Imaging 2019; 49 (04) 1020-1028
    CrossrefPubMedSearch in Google Scholar
  • 101 Schmeel FC, Luetkens JA, Feißt A. et al. Quantitative evaluation of T2* relaxation times for the differentiation of acute benign and malignant vertebral body fractures. Eur J Radiol 2018; 108: 59-65
    CrossrefPubMedSearch in Google Scholar
  • 102 Diefenbach MN, Meineke J, Ruschke S, Baum T, Gersing A, Karampinos DC. On the sensitivity of quantitative susceptibility mapping for measuring trabecular bone density. Magn Reson Med 2019; 81 (03) 1739-1754
    CrossrefPubMedSearch in Google Scholar
  • 103 Larson PE, Gurney PT, Nayak K, Gold GE, Pauly JM, Nishimura DG. Designing long-T2 suppression pulses for ultrashort echo time imaging. Magn Reson Med 2006; 56 (01) 94-103
    CrossrefPubMedSearch in Google Scholar
  • 104 Tyler DJ, Robson MD, Henkelman RM, Young IR, Bydder GM. Magnetic resonance imaging with ultrashort TE (UTE) PULSE sequences: technical considerations. J Magn Reson Imaging 2007; 25 (02) 279-289
    CrossrefPubMedSearch in Google Scholar
  • 105 Du J, Carl M, Bydder M, Takahashi A, Chung CB, Bydder GM. Qualitative and quantitative ultrashort echo time (UTE) imaging of cortical bone. J Magn Reson 2010; 207 (02) 304-311
    CrossrefPubMedSearch in Google Scholar
  • 106 Robson MD, Gatehouse PD, Bydder M, Bydder GM. Magnetic resonance: an introduction to ultrashort TE (UTE) imaging. J Comput Assist Tomogr 2003; 27 (06) 825-846
    CrossrefPubMedSearch in Google Scholar
  • 107 Feuerriegel GC, Kronthaler S, Weiss K. et al. Assessment of glenoid bone loss and other osseous shoulder pathologies comparing MR-based CT-like images with conventional CT. Eur Radiol 2023; 33 (12) 8617-8626
    CrossrefPubMedSearch in Google Scholar
  • 108 Carl M, Bydder GM, Du J. UTE imaging with simultaneous water and fat signal suppression using a time-efficient multispoke inversion recovery pulse sequence. Magn Reson Med 2016; 76 (02) 577-582
    CrossrefPubMedSearch in Google Scholar
  • 109 Ma YJ, Chen Y, Li L. et al. Trabecular bone imaging using a 3D adiabatic inversion recovery prepared ultrashort TE Cones sequence at 3T. Magn Reson Med 2020; 83 (05) 1640-1651
    CrossrefPubMedSearch in Google Scholar
  • 110 Kronthaler S, Boehm C, Feuerriegel G. et al. Assessment of vertebral fractures and edema of the thoracolumbar spine based on water-fat and susceptibility-weighted images derived from a single ultra-short echo time scan. Magn Reson Med 2022; 87 (04) 1771-1783
    CrossrefPubMedSearch in Google Scholar
  • 111 Manhard MK, Harkins KD, Gochberg DF, Nyman JS, Does MD. 30-Second bound and pore water concentration mapping of cortical bone using 2D UTE with optimized half-pulses. Magn Reson Med 2017; 77 (03) 945-950
    CrossrefPubMedSearch in Google Scholar
  • 112 Rajapakse CS, Bashoor-Zadeh M, Li C, Sun W, Wright AC, Wehrli FW. Volumetric cortical bone porosity assessment with MR imaging: validation and clinical feasibility. Radiology 2015; 276 (02) 526-535
    CrossrefPubMedSearch in Google Scholar
  • 113 Liu J, Liao JW, Li W. et al. Assessment of osteoporosis in lumbar spine: in vivo quantitative MR imaging of collagen bound water in trabecular bone. Front Endocrinol (Lausanne) 2022; 13: 801930
    CrossrefPubMedSearch in Google Scholar
  • 114 Li Y, Liang X, Liu J. et al. Assessment of osteoporosis at the lumbar spine using ultrashort echo time magnetization transfer (UTE-MT) MRI. J Magn Reson Imaging 2024; 59 (04) 1285-1298
    CrossrefPubMedSearch in Google Scholar
  • 115 Liu J, Chen JD, Li P. et al. Comprehensive assessment of osteoporosis in lumbar spine using compositional MR imaging of trabecular bone. Eur Radiol 2023; 33 (06) 3995-4006
    CrossrefPubMedSearch in Google Scholar
  • 116 Krug R, Burghardt AJ, Majumdar S, Link TM. High-resolution imaging techniques for the assessment of osteoporosis. Radiol Clin North Am 2010; 48 (03) 601-621
    CrossrefPubMedSearch in Google Scholar
  • 117 Krug R, Banerjee S, Han ET, Newitt DC, Link TM, Majumdar S. Feasibility of in vivo structural analysis of high-resolution magnetic resonance images of the proximal femur. Osteoporos Int 2005; 16 (11) 1307-1314
    CrossrefPubMedSearch in Google Scholar
  • 118 Kazakia GJ, Carballido-Gamio J, Lai A. et al. Trabecular bone microstructure is impaired in the proximal femur of human immunodeficiency virus-infected men with normal bone mineral density. Quant Imaging Med Surg 2018; 8 (01) 5-13
    CrossrefPubMedSearch in Google Scholar
  • 119 Chang G, Deniz CM, Honig S. et al. Feasibility of three-dimensional MRI of proximal femur microarchitecture at 3 tesla using 26 receive elements without and with parallel imaging. J Magn Reson Imaging 2014; 40 (01) 229-238
    CrossrefPubMedSearch in Google Scholar
  • 120 Chang G, Honig S, Brown R. et al. Finite element analysis applied to 3-T MR imaging of proximal femur microarchitecture: lower bone strength in patients with fragility fractures compared with control subjects. Radiology 2014; 272 (02) 464-474
    CrossrefPubMedSearch in Google Scholar
  • 121 Chang G, Rajapakse CS, Regatte RR. et al. 3.  . Tesla MRI detects deterioration in proximal femur microarchitecture and strength in long-term glucocorticoid users compared with controls. J Magn Reson Imaging 2015; 42 (06) 1489-1496
    PubMedSearch in Google Scholar
  • 122 Banerjee S, Han ET, Krug R, Newitt DC, Majumdar S. Application of refocused steady-state free-precession methods at 1.5 and 3 T to in vivo high-resolution MRI of trabecular bone: simulations and experiments. J Magn Reson Imaging 2005; 21 (06) 818-825
    CrossrefPubMedSearch in Google Scholar
  • 123 Han M, Chiba K, Banerjee S, Carballido-Gamio J, Krug R. Variable flip angle three-dimensional fast spin-echo sequence combined with outer volume suppression for imaging trabecular bone structure of the proximal femur. J Magn Reson Imaging 2015; 41 (05) 1300-1310
    CrossrefPubMedSearch in Google Scholar
  • 124 Wald LL, Carvajal L, Moyher SE. et al. Phased array detectors and an automated intensity-correction algorithm for high-resolution MR imaging of the human brain. Magn Reson Med 1995; 34 (03) 433-439
    CrossrefPubMedSearch in Google Scholar
  • 125 Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998; 17 (01) 87-97
    CrossrefPubMedSearch in Google Scholar
  • 126 Vasilic B, Wehrli FW. A novel local thresholding algorithm for trabecular bone volume fraction mapping in the limited spatial resolution regime of in vivo MRI. IEEE Trans Med Imaging 2005; 24 (12) 1574-1585
    CrossrefPubMedSearch in Google Scholar
  • 127 Majumdar S, Genant HK, Grampp S. et al. Correlation of trabecular bone structure with age, bone mineral density, and osteoporotic status: in vivo studies in the distal radius using high resolution magnetic resonance imaging. J Bone Miner Res 1997; 12 (01) 111-118
    CrossrefPubMedSearch in Google Scholar
  • 128 Carballido-Gamio J, Phan C, Link TM, Majumdar S. Characterization of trabecular bone structure from high-resolution magnetic resonance images using fuzzy logic. Magn Reson Imaging 2006; 24 (08) 1023-1029
    CrossrefPubMedSearch in Google Scholar
  • 129 Folkesson J, Carballido-Gamio J, Eckstein F, Link TM, Majumdar S. Local bone enhancement fuzzy clustering for segmentation of MR trabecular bone images. Med Phys 2010; 37 (01) 295-302
    CrossrefPubMedSearch in Google Scholar
  • 130 Chang G, Rajapakse CS, Chen C. et al. 3-T MR imaging of proximal femur microarchitecture in subjects with and without fragility fracture and nonosteoporotic proximal femur bone mineral density. Radiology 2018; 287 (02) 608-619
    CrossrefPubMedSearch in Google Scholar
  • 131 Friston KJ, Frith CD, Liddle PF, Dolan RJ, Lammertsma AA, Frackowiak RS. The relationship between global and local changes in PET scans. J Cereb Blood Flow Metab 1990; 10 (04) 458-466
    CrossrefPubMedSearch in Google Scholar
  • 132 Carballido-Gamio J, Karampinos DC, Lai A. et al. Investigation of the local relationship between bone marrow fat content and bone mineral density in the proximal femur of subjects with and without fragility fractures. Paper presented at The American Society for Bone and Mineral Research; October 6, 2013; Baltimore, MD
    PubMed
  • 133 Carballido-Gamio J, Harnish R, Saeed I. et al. Proximal femoral density distribution and structure in relation to age and hip fracture risk in women. J Bone Miner Res 2013; 28 (03) 537-546
    CrossrefPubMedSearch in Google Scholar
  • 134 Sollmann N, Dieckmeyer M, Schlaeger S. et al. Associations between lumbar vertebral bone marrow and paraspinal muscle fat compositions-an investigation by chemical shift encoding-based water-fat MRI. Front Endocrinol (Lausanne) 2018; 9: 563
    CrossrefPubMedSearch in Google Scholar
  • 135 Sollmann N, Bonnheim NB, Joseph GB. et al. Paraspinal muscle in chronic low back pain: comparison between standard parameters and chemical shift encoding-based water-fat MRI. J Magn Reson Imaging 2022; 56 (05) 1600-1608
    CrossrefPubMedSearch in Google Scholar
  • 136 Gassert FT, Glanz L, Boehm C. et al. Associations between bone mineral density and longitudinal changes of vertebral bone marrow and paraspinal muscle composition assessed using MR-based proton density fat fraction and T2* maps in patients with and without osteoporosis. Diagnostics (Basel) 2022; 12 (10) 12
    PubMedSearch in Google Scholar
  • 137 Badr S, Dapvril H, Lombardo D. et al. Myosteatosis and bone marrow adiposity are not associated among postmenopausal women with fragility fractures. Front Endocrinol (Lausanne) 2023; 14: 1178464
    CrossrefPubMedSearch in Google Scholar
  • 138 Sollmann N, Zoffl A, Franz D. et al. Regional variation in paraspinal muscle composition using chemical shift encoding-based water-fat MRI. Quant Imaging Med Surg 2020; 10 (02) 496-507
    CrossrefPubMedSearch in Google Scholar
  • 139 Burian E, Syväri J, Dieckmeyer M. et al. Age- and BMI-related variations of fat distribution in sacral and lumbar bone marrow and their association with local muscle fat content. Sci Rep 2020; 10 (01) 9686
    CrossrefPubMedSearch in Google Scholar
  • 140 Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 2017; 37 (05) 1483-1503
    CrossrefPubMedSearch in Google Scholar
  • 141 Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol 2004; 59 (12) 1061-1069
    CrossrefPubMedSearch in Google Scholar
  • 142 Burian E, Subburaj K, Mookiah MRK. et al. Texture analysis of vertebral bone marrow using chemical shift encoding-based water-fat MRI: a feasibility study. Osteoporos Int 2019; 30 (06) 1265-1274
    CrossrefPubMedSearch in Google Scholar
  • 143 Dieckmeyer M, Junker D, Ruschke S. et al. Vertebral bone marrow heterogeneity using texture analysis of chemical shift encoding-based MRI: variations in age, sex, and anatomical location. Front Endocrinol (Lausanne) 2020; 11: 555931
    CrossrefPubMedSearch in Google Scholar
  • 144 Sollmann N, Becherucci EA, Boehm C. et al. Texture analysis using CT and chemical shift encoding-based water-fat MRI can improve differentiation between patients with and without osteoporotic vertebral fractures. Front Endocrinol (Lausanne) 2022; 12: 778537
    CrossrefPubMedSearch in Google Scholar
  • 145 Richmond BG, Wright BW, Grosse I. et al. Finite element analysis in functional morphology. Anat Rec A Discov Mol Cell Evol Biol 2005; 283 (02) 259-274
    CrossrefPubMedSearch in Google Scholar
  • 146 Zysset PK, Dall'ara E, Varga P, Pahr DH. Finite element analysis for prediction of bone strength. Bonekey Rep 2013; 2: 386
    CrossrefPubMedSearch in Google Scholar
  • 147 Rajapakse CS, Farid AR, Kargilis DC. et al. MRI-based assessment of proximal femur strength compared to mechanical testing. Bone 2020; 133: 115227
    CrossrefPubMedSearch in Google Scholar
  • 148 Rajapakse CS, Hotca A, Newman BT. et al. Patient-specific hip fracture strength assessment with microstructural MR imaging-based finite element modeling. Radiology 2017; 283 (03) 854-861
    CrossrefPubMedSearch in Google Scholar
  • 149 Chang G, Hotca-Cho A, Rusinek H. et al. Measurement reproducibility of magnetic resonance imaging-based finite element analysis of proximal femur microarchitecture for in vivo assessment of bone strength. Magn Reson Mater Biol Phys Med 2015; 28 (04) 407-412
    CrossrefPubMedSearch in Google Scholar