Abstract
Osteoporosis is a metabolic bone disease with a high prevalence that affects the population worldwide, particularly the elderly. It is often due to fractures associated with bone fragility that the diagnosis of osteoporosis becomes clinically evident. However, early diagnosis would be necessary to initiate therapy and to prevent occurrence of further fractures, thus reducing morbidity and mortality. X-ray-based imaging plays a key role for fracture risk assessment and monitoring of osteoporosis. Whereas over decades dual-energy X-ray absorptiometry (DXA) has been the main method used and still reflects the reference standard, another modality reemerges with quantitative computed tomography (QCT) because of its three-dimensional advantages and the opportunistic exploitation of routine CT scans. Against this background, this article intends to review and evaluate recent advances in the field of X-ray-based quantitative imaging of osteoporosis at the spine. First, standard DXA with the recent addition of trabecular bone score (TBS) is presented. Secondly, standard QCT, dual-energy BMD quantification, and opportunistic BMD screening in non-dedicated CT exams are discussed. Lastly, finite element analysis and microstructural parameter analysis are reviewed.
Similar content being viewed by others
Abbreviations
- BMD:
-
Bone mineral density
- CBM:
-
Cortical bone mapping
- CV:
-
Coefficient of variation
- DXA:
-
Dual-energy X-ray absorptiometry
- DECT:
-
Dual-energy computed tomography
- FEA:
-
Finite element analysis
- FRAX:
-
Fracture Risk Algorithm
- HU:
-
Hounsfield units
- HRCT:
-
High-resolution computed tomography
- MDCT:
-
Multi-detector computed tomography
- ICC:
-
Interclass correlation coefficient
- QCT:
-
Quantitative computed tomography
- SDCT:
-
Spectral detector computed tomography
- SIR:
-
Statistical iterative reconstruction
- SPM:
-
Statistical parametric mapping
- TBS:
-
Trabecular bone score
References
Johnell O, Kanis JA (2006) An estimate of the worldwide prevalence and disability associated with osteoporotic fractures. Osteoporos Int 17:1726–1733. https://doi.org/10.1007/s00198-006-0172-4
National Institutes of Health (2001) Osteoporosis prevention, diagnosis, and therapy. JAMA 285:785–795
Hernlund E, Svedbom A, Ivergård M et al (2013) Osteoporosis in the European Union: medical management, epidemiology and economic burden. A report prepared in collaboration with the International Osteoporosis Foundation (IOF) and the European Federation of Pharmaceutical Industry Associations (EFPIA). Arch Osteoporos 8:136. https://doi.org/10.1007/s11657-013-0136-1
Hallberg I, Bachrach-Lindström M, Hammerby S et al (2009) Health-related quality of life after vertebral or hip fracture: a seven-year follow-up study. BMC Musculoskelet Disord 10:135. https://doi.org/10.1186/1471-2474-10-135
Bliuc D, Nguyen ND, Nguyen TV et al (2013) Compound risk of high mortality following osteoporotic fracture and refracture in elderly women and men. J Bone Miner Res 28:2317–2324. https://doi.org/10.1002/jbmr.1968
Melton LJ, Atkinson EJ, Cooper C et al (1999) Vertebral fractures predict subsequent fractures. Osteoporos Int 10:214–221
Eckstein F, Lochmüller E-M, Lill CA et al (2002) Bone strength at clinically relevant sites displays substantial heterogeneity and is best predicted from site-specific bone densitometry. J Bone Miner Res 17:162–171. https://doi.org/10.1359/jbmr.2002.17.1.162
Link TM, Bauer J, Kollstedt A et al (2004) Trabecular bone structure of the distal radius, the calcaneus, and the spine: which site predicts fracture status of the spine best? Investig Radiol 39:487–497
World Health Organization (2007) Assessment of osteoporosis at the primary health care level. Summary Report of a WHO Scientific Group. WHO, Geneva
Kanis JA, Hans D, Cooper C et al (2011) Interpretation and use of FRAX in clinical practice. Osteoporos Int 22:2395–2411. https://doi.org/10.1007/s00198-011-1713-z
Shevroja E, Lamy O, Kohlmeier L et al (2017) Use of trabecular bone score (TBS) as a Complementary Approach to Dual-energy X-ray Absorptiometry (DXA) for Fracture Risk Assessment in Clinical Practice. J Clin Densitom 20:334–345. https://doi.org/10.1016/j.jocd.2017.06.019
Organisation européenne de coopération économique (2017) Health at a glance 2017: OECD indicators. 9. Health care activities - Medical technologies. OECD, Paris
Willemink MJ, Noël PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29:2185–2195. https://doi.org/10.1007/s00330-018-5810-7
Kanis JA, Melton LJ, Christiansen C et al (1994) The diagnosis of osteoporosis. J Bone Miner Res 9:1137–1141. https://doi.org/10.1002/jbmr.5650090802
Kanis JA (1994) Assessment of fracture risk and its application to screening for postmenopausal osteoporosis: synopsis of a WHO report. WHO Study Group. Osteoporos Int 4:368–381
Cosman F, de Beur SJ, LeBoff MS et al (2014) Clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int 25:2359–2381. https://doi.org/10.1007/s00198-014-2794-2
Mazess R, Chesnut CH, McClung M, Genant H (1992) Enhanced precision with dual-energy X-ray absorptiometry. Calcif Tissue Int 51:14–17
Marshall D, Johnell O, Wedel H (1996) Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 312:1254–1259
Baim S, Wilson CR, Lewiecki EM et al (2005) Precision assessment and radiation safety for dual-energy X-ray absorptiometry: position paper of the International Society for Clinical Densitometry. J Clin Densitom 8:371–378
Cranney A, Tugwell P, Wells G et al (2002) Meta-analyses of therapies for postmenopausal osteoporosis. I. Systematic reviews of randomized trials in osteoporosis: introduction and methodology. Endocr Rev 23:496–507. https://doi.org/10.1210/er.2001-1002
Engelke K (2017) Quantitative computed tomography-current status and new developments. J Clin Densitom 20:309–321. https://doi.org/10.1016/j.jocd.2017.06.017
Yu W, Glüer CC, Fuerst T et al (1995) Influence of degenerative joint disease on spinal bone mineral measurements in postmenopausal women. Calcif Tissue Int 57:169–174
Promma S, Sritara C, Wipuchwongsakorn S et al (2018) Errors in patient positioning for bone mineral density assessment by dual X-ray absorptiometry: effect of technologist retraining. J Clin Densitom 21:252–259. https://doi.org/10.1016/j.jocd.2017.07.004
Bolotin HH (2007) DXA in vivo BMD methodology: an erroneous and misleading research and clinical gauge of bone mineral status, bone fragility, and bone remodelling. Bone 41:138–154. https://doi.org/10.1016/j.bone.2007.02.022
Lewiecki EM, Binkley N, Morgan SL et al (2016) Best practices for dual-energy X-ray absorptiometry measurement and reporting: International Society for Clinical Densitometry Guidance. J Clin Densitom 19:127–140. https://doi.org/10.1016/j.jocd.2016.03.003
Damiano J, Kolta S, Porcher R et al (2006) Diagnosis of vertebral fractures by vertebral fracture assessment. J Clin Densitom 9:66–71. https://doi.org/10.1016/j.jocd.2005.11.002
Pothuaud L, Carceller P, Hans D (2008) Correlations between grey-level variations in 2D projection images (TBS) and 3D microarchitecture: applications in the study of human trabecular bone microarchitecture. Bone 42:775–787. https://doi.org/10.1016/j.bone.2007.11.018
Hans D, Barthe N, Boutroy S et al (2011) Correlations between trabecular bone score, measured using anteroposterior dual-energy X-ray absorptiometry acquisition, and 3-dimensional parameters of bone microarchitecture: an experimental study on human cadaver vertebrae. J Clin Densitom 14:302–312. https://doi.org/10.1016/j.jocd.2011.05.005
Hans D, Goertzen AL, Krieg M-A, 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.499
Iki M, Tamaki J, Kadowaki E et al (2014) Trabecular bone score (TBS) predicts vertebral fractures in Japanese women over 10 years independently of bone density and prevalent vertebral deformity: the Japanese Population-Based Osteoporosis (JPOS) cohort study. J Bone Miner Res 29:399–407. https://doi.org/10.1002/jbmr.2048
Boutroy S, Hans D, Sornay-Rendu E et al (2013) Trabecular bone score improves fracture risk prediction in non-osteoporotic women: the OFELY study. Osteoporos Int 24:77–85. https://doi.org/10.1007/s00198-012-2188-2
American College of Radiology (2018) ACR–SPR–SSR practice parameter for the performance of musculoskeletal quantitative computed tomography (QCT). American College of Radiology, Reston. Available via https://www.acr.org/-/media/ACR/Files/Practice-Parameters/QCT.pdf?la = en. Accessed 7 Nov 2018
Guglielmi G, Grimston SK, Fischer KC, Pacifici R (1994) Osteoporosis: diagnosis with lateral and posteroanterior dual x-ray absorptiometry compared with quantitative CT. Radiology 192:845–850. https://doi.org/10.1148/radiology.192.3.8058958
Grampp S, Genant HK, Mathur A et al (1997) Comparisons of noninvasive bone mineral measurements in assessing age-related loss, fracture discrimination, and diagnostic classification. J Bone Miner Res 12:697–711. https://doi.org/10.1359/jbmr.1997.12.5.697
Gruber R, Pietschmann P, Peterlik M (2008) Introduction to bone development, remodelling and repair. In: Grampp S (ed) Radiology of Osteoporosis. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 1–23
Genant HK, Engelke K, Bolognese MA et al (2017) Effects of romosozumab compared with teriparatide on bone density and mass at the spine and hip in postmenopausal women with low bone mass. J Bone Miner Res 32:181–187. https://doi.org/10.1002/jbmr.2932
Bligh M, Bidaut L, White RA et al (2009) Helical multidetector row quantitative computed tomography (QCT) precision. Acad Radiol 16:150–159. https://doi.org/10.1016/j.acra.2008.08.007
Garner HW, Paturzo MM, Gaudier G et al (2017) Variation in attenuation in L1 trabecular bone at different tube voltages: caution is warranted when screening for osteoporosis with the use of opportunistic CT. AJR Am J Roentgenol 208:165–170. https://doi.org/10.2214/AJR.16.16744
Mei K, Kopp FK, Bippus R et al (2017) Is multidetector CT-based bone mineral density and quantitative bone microstructure assessment at the spine still feasible using ultra-low tube current and sparse sampling? Eur Radiol 27:5261–5271. https://doi.org/10.1007/s00330-017-4904-y
Engelke K, Mastmeyer A, Bousson V et al (2009) Reanalysis precision of 3D quantitative computed tomography (QCT) of the spine. Bone 44:566–572. https://doi.org/10.1016/j.bone.2008.11.008
Pfeilschifter J, Diel IJ (2000) Osteoporosis due to cancer treatment: pathogenesis and management. J Clin Oncol 18:1570–1593. https://doi.org/10.1200/JCO.2000.18.7.1570
Löffler MT, Jacob A, Valentinitsch A et al (2019) Improved prediction of incident vertebral fractures using opportunistic QCT compared to DXA. Eur Radiol. https://doi.org/10.1007/s00330-019-06018-w
Brown JK, Timm W, Bodeen G et al (2017) Asynchronously calibrated quantitative bone densitometry. J Clin Densitom 20:216–225. https://doi.org/10.1016/j.jocd.2015.11.001
Boden SD, Goodenough DJ, Stockham CD et al (1989) Precise measurement of vertebral bone density using computed tomography without the use of an external reference phantom. J Digit Imaging 2:31–38
Alacreu E, Moratal D, Arana E (2017) Opportunistic screening for osteoporosis by routine CT in Southern Europe. Osteoporos Int 28:983–990. https://doi.org/10.1007/s00198-016-3804-3
Mueller DK, Kutscherenko A, Bartel H et al (2011) Phantom-less QCT BMD system as screening tool for osteoporosis without additional radiation. Eur J Radiol 79:375–381. https://doi.org/10.1016/j.ejrad.2010.02.008
Wang L, Su Y, Wang Q et al (2017) Validation of asynchronous quantitative bone densitometry of the spine: accuracy, short-term reproducibility, and a comparison with conventional quantitative computed tomography. Sci Rep 7:6284. https://doi.org/10.1038/s41598-017-06608-y
Budoff MJ, Malpeso JM, Zeb I et al (2013) Measurement of phantomless thoracic bone mineral density on coronary artery calcium CT scans acquired with various CT scanner models. Radiology 267:830–836. https://doi.org/10.1148/radiol.13111987
Lee DC, Hoffmann PF, Kopperdahl DL, Keaveny TM (2017) Phantomless calibration of CT scans for measurement of BMD and bone strength-inter-operator reanalysis precision. Bone 103:325–333. https://doi.org/10.1016/j.bone.2017.07.029
Cann CE (1988) Quantitative CT for determination of bone mineral density: a review. Radiology 166:509–522. https://doi.org/10.1148/radiology.166.2.3275985
Genant HK, Boyd D (1977) Quantitative bone mineral analysis using dual energy computed tomography. Investig Radiol 12:545–551
Birnbaum BA, Hindman N, Lee J, Babb JS (2007) Multi-detector row CT attenuation measurements: assessment of intra- and interscanner variability with an anthropomorphic body CT phantom. Radiology 242:109–119. https://doi.org/10.1148/radiol.2421052066
Shepherd JA, Schousboe JT, Broy SB et al (2015) Executive summary of the 2015 ISCD Position Development Conference on advanced measures from DXA and QCT: fracture prediction beyond BMD. J Clin Densitom 18:274–286. https://doi.org/10.1016/j.jocd.2015.06.013
Engelke K, Lang T, Khosla S et al (2015) Clinical use of quantitative computed tomography-based advanced techniques in the management of osteoporosis in adults: the 2015 ISCD Official Positions-part III. J Clin Densitom 18:393–407. https://doi.org/10.1016/j.jocd.2015.06.010
Mallinson PI, Coupal TM, McLaughlin PD et al (2016) Dual-energy CT for the musculoskeletal system. Radiology 281:690–707. https://doi.org/10.1148/radiol.2016151109
Mei K, Schwaiger BJ, Kopp FK et al (2017) Bone mineral density measurements in vertebral specimens and phantoms using dual-layer spectral computed tomography. Sci Rep 7:17519. https://doi.org/10.1038/s41598-017-17855-4
van Hamersvelt RW, Schilham AMR, Engelke K et al (2017) Accuracy of bone mineral density quantification using dual-layer spectral detector CT: a phantom study. Eur Radiol 27:4351–4359. https://doi.org/10.1007/s00330-017-4801-4
Van Hedent S, Su K-H, Jordan DW et al (2019) Improving bone mineral density assessment using spectral detector CT. J Clin Densitom 22:374–381. https://doi.org/10.1016/j.jocd.2018.10.004
Wichmann JL, Booz C, Wesarg S et al (2014) Dual-energy CT-based phantomless in vivo three-dimensional bone mineral density assessment of the lumbar spine. Radiology 271:778–784. https://doi.org/10.1148/radiol.13131952
Roski F, Hammel J, Mei K et al (2019) Bone mineral density measurements derived from dual-layer spectral CT enable opportunistic screening for osteoporosis. Eur Radiol. https://doi.org/10.1007/s00330-019-06263-z
McCarthy I (2006) The physiology of bone blood flow: a review. J Bone Joint Surg Am 88(Suppl 3):4–9. https://doi.org/10.2106/JBJS.F.00890
Acu K, Scheel M, Issever AS (2014) Time dependency of bone density estimation from computed tomography with intravenous contrast agent administration. Osteoporos Int 25:535–542. https://doi.org/10.1007/s00198-013-2440-4
Toelly A, Bardach C, Weber M et al (2017) Influence of contrast media on bone mineral density (BMD) measurements from routine contrast-enhanced MDCT datasets using a phantom-less BMD measurement tool. Rofo 189:537–543. https://doi.org/10.1055/s-0043-102941
Abdullayev N, Neuhaus V-F, Bratke G et al (2018) Effects of contrast enhancement on in-body calibrated phantomless bone mineral density measurements in computed tomography. J Clin Densitom 21:360–366. https://doi.org/10.1016/j.jocd.2017.10.001
Kaesmacher J, Liebl H, Baum T, Kirschke JS (2017) Bone mineral density estimations from routine multidetector computed tomography: a comparative study of contrast and calibration effects. J Comput Assist Tomogr 41:217–223. https://doi.org/10.1097/RCT.0000000000000518
Baum T, Müller D, Dobritz M et al (2011) BMD measurements of the spine derived from sagittal reformations of contrast-enhanced MDCT without dedicated software. Eur J Radiol 80:e140–e145. https://doi.org/10.1016/j.ejrad.2010.08.034
Glüer CC, Blake G, Lu Y et al (1995) Accurate assessment of precision errors: how to measure the reproducibility of bone densitometry techniques. Osteoporos Int 5:262–270
Pompe E, de Jong PA, de Jong WU et al (2016) Inter-observer and inter-examination variability of manual vertebral bone attenuation measurements on computed tomography. Eur Radiol 26:3046–3053. https://doi.org/10.1007/s00330-015-4145-x
Therkildsen J, Winther S, Nissen L et al (2018) Feasibility of opportunistic screening for low thoracic bone mineral density in patients referred for routine cardiac CT. J Clin Densitom. https://doi.org/10.1016/j.jocd.2018.12.002
Gausden EB, Nwachukwu BU, Schreiber JJ et al (2017) Opportunistic use of CT imaging for osteoporosis screening and bone density assessment: a qualitative systematic review. J Bone Joint Surg Am 99:1580–1590. https://doi.org/10.2106/JBJS.16.00749
Zysset P, Qin L, Lang T et al (2015) Clinical use of quantitative computed tomography-based finite element analysis of the hip and spine in the management of osteoporosis in adults: the 2015 ISCD Official Positions-Part II. J Clin Densitom 18:359–392. https://doi.org/10.1016/j.jocd.2015.06.011
Anitha D, Subburaj K, Mei K et al (2016) Effects of dose reduction on bone strength prediction using finite element analysis. Sci Rep 6:38441. https://doi.org/10.1038/srep38441
Anitha D, Mei K, Dieckmeyer M et al (2018) MDCT-based finite element analysis of vertebral fracture risk: what dose is needed? Clin Neuroradiol. https://doi.org/10.1007/s00062-018-0722-0
Keaveny TM (2010) Biomechanical computed tomography-noninvasive bone strength analysis using clinical computed tomography scans. Ann N Y Acad Sci 1192:57–65. https://doi.org/10.1111/j.1749-6632.2009.05348.x
Johannesdottir F, Allaire B, Bouxsein ML (2018) Fracture prediction by computed tomography and finite element analysis: current and future perspectives. Curr Osteoporos Rep. https://doi.org/10.1007/s11914-018-0450-z
Wang X, Sanyal A, Cawthon PM et al (2012) Prediction of new clinical vertebral fractures in elderly men using finite element analysis of CT scans. J Bone Miner Res 27:808–816. https://doi.org/10.1002/jbmr.1539
Kopperdahl DL, Aspelund T, Hoffmann PF et al (2014) Assessment of incident spine and hip fractures in women and men using finite element analysis of CT scans. J Bone Miner Res 29:570–580. https://doi.org/10.1002/jbmr.2069
Allaire BT, Lu D, Johannesdottir F et al (2019) Prediction of incident vertebral fracture using CT-based finite element analysis. Osteoporos Int 30:323–331. https://doi.org/10.1007/s00198-018-4716-1
Fidler JL, Murthy NS, Khosla S et al (2016) Comprehensive assessment of osteoporosis and bone fragility with CT colonography. Radiology 278:172–180. https://doi.org/10.1148/radiol.2015141984
Graeff C, Marin F, Petto H et al (2013) High resolution quantitative computed tomography-based assessment of trabecular microstructure and strength estimates by finite-element analysis of the spine, but not DXA, reflects vertebral fracture status in men with glucocorticoid-induced osteoporosis. Bone 52:568–577. https://doi.org/10.1016/j.bone.2012.10.036
Graeff C, Chevalier Y, Charlebois M et al (2009) Improvements in vertebral body strength under teriparatide treatment assessed in vivo by finite element analysis: results from the EUROFORS study. J Bone Miner Res 24:1672–1680. https://doi.org/10.1359/jbmr.090416
Graeff C, Campbell GM, Peña J et al (2015) Administration of romosozumab improves vertebral trabecular and cortical bone as assessed with quantitative computed tomography and finite element analysis. Bone 81:364–369. https://doi.org/10.1016/j.bone.2015.07.036
Dempster DW, Compston JE, Drezner MK et al (2013) Standardized nomenclature, symbols, and units for bone histomorphometry: a 2012 update of the report of the ASBMR Histomorphometry Nomenclature Committee. J Bone Miner Res 28:2–17. https://doi.org/10.1002/jbmr.1805
Weinstein RS, Majumdar S (1994) Fractal geometry and vertebral compression fractures. J Bone Miner Res 9:1797–1802. https://doi.org/10.1002/jbmr.5650091117
Odgaard A, Gundersen HJ (1993) Quantification of connectivity in cancellous bone, with special emphasis on 3-D reconstructions. Bone 14:173–182
Harrigan TP, Mann RW (1984) Characterization of microstructural anisotropy in orthotropic materials using a second rank tensor. J Mater Sci 19:761–767. https://doi.org/10.1007/BF00540446
Chen C, Zhang X, Guo J et al (2018) Quantitative imaging of peripheral trabecular bone microarchitecture using MDCT. Med Phys 45:236–249. https://doi.org/10.1002/mp.12632
Ito M, Ikeda K, Nishiguchi M et al (2005) Multi-detector row CT imaging of vertebral microstructure for evaluation of fracture risk. J Bone Miner Res 20:1828–1836. https://doi.org/10.1359/JBMR.050610
Graeff C, Timm W, Nickelsen TN et al (2007) Monitoring teriparatide-associated changes in vertebral microstructure by high-resolution CT in vivo: results from the EUROFORS study. J Bone Miner Res 22:1426–1433. https://doi.org/10.1359/jbmr.070603
Krebs A, Graeff C, Frieling I et al (2009) High resolution computed tomography of the vertebrae yields accurate information on trabecular distances if processed by 3D fuzzy segmentation approaches. Bone 44:145–152. https://doi.org/10.1016/j.bone.2008.08.131
Baum T, Gräbeldinger M, Räth C et al (2014) Trabecular bone structure analysis of the spine using clinical MDCT: can it predict vertebral bone strength? J Bone Miner Metab 32:56–64. https://doi.org/10.1007/s00774-013-0465-6
Kopp FK, Holzapfel K, Baum T et al (2016) Effect of low-dose MDCT and iterative reconstruction on trabecular bone microstructure assessment. PLoS One 11:e0159903. https://doi.org/10.1371/journal.pone.0159903
Damm T, Peña JA, Campbell GM et al (2019) Improved accuracy in the assessment of vertebral cortical thickness by quantitative computed tomography using the Iterative Convolution OptimizatioN (ICON) method. Bone 120:194–203. https://doi.org/10.1016/j.bone.2018.08.024
Mookiah MRK, Rohrmeier A, Dieckmeyer M et al (2018) Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis. Osteoporos Int 29:825–835. https://doi.org/10.1007/s00198-017-4342-3
Mookiah MRK, Subburaj K, Mei K et al (2018) Multidetector computed tomography imaging: effect of sparse sampling and iterative reconstruction on trabecular bone microstructure. J Comput Assist Tomogr 42:441–447. https://doi.org/10.1097/RCT.0000000000000710
Valentinitsch A, Trebeschi S, Kaesmacher J et al (2019) Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures. Osteoporos Int. https://doi.org/10.1007/s00198-019-04910-1
Treece G, Gee A (2018) Cortical bone mapping: measurement and statistical analysis of localised skeletal changes. Curr Osteoporos Rep 16:617–625. https://doi.org/10.1007/s11914-018-0475-3
Whitmarsh T, Treece G, Gee A et al (2014) Romosozumab and teriparatide effects on vertebral cortical mass, thickness, and density in postmenopausal women with low bone mineral density (BMD). JOURNAL OF BONE AND MINERAL RESEARCH, In, pp S18–S18
Valentinitsch A, Trebeschi S, Alarcón E et al (2017) Regional analysis of age-related local bone loss in the spine of a healthy population using 3D voxel-based modeling. Bone 103:233–240. https://doi.org/10.1016/j.bone.2017.06.013
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
None.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Löffler, M., Sollmann, N., Mei, K. et al. X-ray-based quantitative osteoporosis imaging at the spine. Osteoporos Int 31, 233–250 (2020). https://doi.org/10.1007/s00198-019-05212-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00198-019-05212-2