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Open Access 01-09-2010 | Original Article

Automated 3D trabecular bone structure analysis of the proximal femur—prediction of biomechanical strength by CT and DXA

Authors: T. Baum, J. Carballido-Gamio, M. B. Huber, D. Müller, R. Monetti, C. Räth, F. Eckstein, E. M. Lochmüller, S. Majumdar, E. J. Rummeny, T. M. Link, J. S. Bauer

Published in: Osteoporosis International | Issue 9/2010

Abstract

Summary

The standard diagnostic technique for assessing osteoporosis is dual X-ray absorptiometry (DXA) measuring bone mass parameters. In this study, a combination of DXA and trabecular structure parameters (acquired by computed tomography [CT]) most accurately predicted the biomechanical strength of the proximal femur and allowed for a better prediction than DXA alone.

Introduction

An automated 3D segmentation algorithm was applied to determine specific structure parameters of the trabecular bone in CT images of the proximal femur. This was done to evaluate the ability of these parameters for predicting biomechanical femoral bone strength in comparison with bone mineral content (BMC) and bone mineral density (BMD) acquired by DXA as standard diagnostic technique.

Methods

One hundred eighty-seven proximal femur specimens were harvested from formalin-fixed human cadavers. BMC and BMD were determined by DXA. Structure parameters of the trabecular bone (i.e., morphometry, fuzzy logic, Minkowski functionals, and the scaling index method [SIM]) were computed from CT images. Absolute femoral bone strength was assessed with a biomechanical side-impact test measuring failure load (FL). Adjusted FL parameters for appraisal of relative bone strength were calculated by dividing FL by influencing variables such as body height, weight, or femoral head diameter.

Results

The best single parameter predicting FL and adjusted FL parameters was apparent trabecular separation (morphometry) or DXA-derived BMC or BMD with correlations up to r = 0.802. In combination with DXA, structure parameters (most notably the SIM and morphometry) added in linear regression models significant information in predicting FL and all adjusted FL parameters (up to R _adj = 0.872) and allowed for a significant better prediction than DXA alone.

Conclusion

A combination of bone mass (DXA) and structure parameters of the trabecular bone (linear and nonlinear, global and local) most accurately predicted absolute and relative femoral bone strength.

Boonen S, Autier P, Barette M, Vanderschueren D, Lips P, Haentjens P (2004) Functional outcome and quality of life following hip fracture in elderly women: a prospective controlled study. Osteoporos Int 15(2):87–94CrossRefPubMed

Jiang HX, Majumdar SR, Dick DA, Moreau M, Raso J, Otto DD, Johnston DW (2005) Development and initial validation of a risk score for predicting in-hospital and 1-year mortality in patients with hip fractures. J Bone Miner Res 20(3):494–500CrossRefPubMed

Damilakis J, Maris TG, Karantanas AH (2007) An update on the assessment of osteoporosis using radiologic techniques. Eur Radiol 17(6):1591–1602CrossRefPubMed

Black DM, Greenspan SL, Ensrud KE, Palermo L, McGowan JA, Lang TF, Garnero P, Bouxsein ML, Bilezikian JP, Rosen CJ (2003) The effects of parathyroid hormone and alendronate alone or in combination in postmenopausal osteoporosis. N Engl J Med 349(13):1207–1215CrossRefPubMed

Boehm HF, Eckstein F, Wunderer C, Kuhn V, Lochmueller EM, Schreiber K, Mueller D, Rummeny EJ, Link TM (2005) Improved performance of hip DXA using a novel region of interest in the upper part of the femoral neck: in vitro study using bone strength as a standard of reference. J Clin Densitom 8(4):488–494CrossRefPubMed

Bousson V, Le Bras A, Roqueplan F, Kang Y, Mitton D, Kolta S, Bergot C, Skalli W, Vicaut E, Kalender W, Engelke K, Laredo JD (2006) Volumetric quantitative computed tomography of the proximal femur: relationships linking geometric and densitometric variables to bone strength. Role for compact bone. Osteoporos Int 17(6):855–864CrossRefPubMed

Lang TF, Keyak JH, Heitz MW, Augat P, Lu Y, Mathur A, Genant HK (1997) Volumetric quantitative computed tomography of the proximal femur: precision and relation to bone strength. Bone 21(1):101–108CrossRefPubMed

Johnell O, Kanis JA, Oden A, Johansson H, De Laet C, Delmas P, Eisman JA, Fujiwara S, Kroger H, Mellstrom D, Meunier PJ, Melton LJ III, O'Neill T, Pols H, Reeve J, Silman A, Tenenhouse A (2005) Predictive value of BMD for hip and other fractures. J Bone Miner Res 20(7):1185–1194CrossRefPubMed

Schuit SC, van der Klift M, Weel AE, de Laet CE, Burger H, Seeman E, Hofman A, Uitterlinden AG, van Leeuwen JP, Pols HA (2004) Fracture incidence and association with bone mineral density in elderly men and women: the Rotterdam Study. Bone 34(1):195–202CrossRefPubMed

10.

Carballido-Gamio J, Majumdar S (2006) Clinical utility of microarchitecture measurements of trabecular bone. Curr Osteoporos Rep 4(2):64–70CrossRefPubMed

11.

Link TM, Vieth V, Stehling C, Lotter A, Beer A, Newitt D, Majumdar S (2003) High-resolution MRI vs multislice spiral CT: which technique depicts the trabecular bone structure best? Eur Radiol 13(4):663–671PubMed

12.

Phan CM, Matsuura M, Bauer JS, Dunn TC, Newitt D, Lochmueller EM, Eckstein F, Majumdar S, Link TM (2006) Trabecular bone structure of the calcaneus: comparison of MR imaging at 3.0 and 1.5T with micro-CT as the standard of reference. Radiology 239(2):488–496CrossRefPubMed

13.

Bauer JS, Kohlmann S, Eckstein F, Mueller D, Lochmuller EM, Link TM (2006) Structural analysis of trabecular bone of the proximal femur using multislice computed tomography: a comparison with dual X-ray absorptiometry for predicting biomechanical strength in vitro. Calcif Tissue Int 78(2):78–89CrossRefPubMed

14.

Link TM, Vieth V, Langenberg R, Meier N, Lotter A, Newitt D, Majumdar S (2003) Structure analysis of high resolution magnetic resonance imaging of the proximal femur: in vitro correlation with biomechanical strength and BMD. Calcif Tissue Int 72(2):156–165CrossRefPubMed

15.

Wachter NJ, Augat P, Mentzel M, Sarkar MR, Krischak GD, Kinzl L, Claes LE (2001) Predictive value of bone mineral density and morphology determined by peripheral quantitative computed tomography for cancellous bone strength of the proximal femur. Bone 28(1):133–139CrossRefPubMed

16.

Boehm HF, Link TM, Monetti R, Kuhn V, Eckstein F, Raeth C, Reiser M (2006) Analysis of the topological properties of the proximal femur on a regional scale: evaluation of multi-detector CT-scans for the assessment of biomechanical strength using local Minkowski functionals in 3D. Proc SPIE 61446X.1:61446X.8

17.

Boehm HF, Link TM, Monetti R, Mueller D, Rummeny EJ, Newitt D, Majumdar S, Raeth C (2004) Application of the Minkowski functionals in 3D to high-resolution MR images of trabecular bone: prediction of the biomechanical strength by nonlinear topological measures. Proc SPIE 5370:172–180CrossRef

18.

Boehm HF, Raeth C, Monetti RA, Mueller D, Newitt D, Majumdar S, Rummeny E, Morfill G, Link TM (2003) Local 3D scaling properties for the analysis of trabecular bone extracted from high-resolution magnetic resonance imaging of human trabecular bone: comparison with bone mineral density in the prediction of biomechanical strength in vitro. Invest Radiol 38(5):269–280CrossRefPubMed

19.

Carballido-Gamio J, Phan C, Link TM, Majumdar S (2006) Characterization of trabecular bone structure from high-resolution magnetic resonance images using fuzzy logic. Magn Reson Imaging 24(8):1023–1029CrossRefPubMed

20.

Mueller D, Link TM, Monetti R, Bauer J, Boehm H, Seifert-Klauss V, Rummeny EJ, Morfill GE, Raeth C (2006) The 3D-based scaling index algorithm: a new structure measure to analyze trabecular bone architecture in high-resolution MR images in vivo. Osteoporos Int 17(10):1483–1493CrossRefPubMed

21.

Patel PV, Eckstein F, Carballido-Gamio J, Phan C, Matsuura M, Lochmuller EM, Majumdar S, Link TM (2007) Fuzzy logic structure analysis of trabecular bone of the calcaneus to estimate proximal femur fracture load and discriminate subjects with and without vertebral fractures using high-resolution magnetic resonance imaging at 1.5 T and 3 T. Calcif Tissue Int 81(4):294–304CrossRefPubMed

22.

Issever AS, Vieth V, Lotter A, Meier N, Laib A, Newitt D, Majumdar S, Link TM (2002) Local differences in the trabecular bone structure of the proximal femur depicted with high-spatial-resolution MR imaging and multisection CT. Acad Radiol 9(12):1395–1406CrossRefPubMed

23.

Nazarian A, Muller J, Zurakowski D, Muller R, Snyder BD (2007) Densitometric, morphometric and mechanical distributions in the human proximal femur. J Biomech 40(11):2573–2579CrossRefPubMed

24.

Huber MB, Carballido-Gamio J, Bauer JS, Baum T, Eckstein F, Lochmuller EM, Majumdar S, Link TM (2008) Proximal femur specimens: automated 3D trabecular bone mineral density analysis at multidetector CT—correlation with biomechanical strength measurement. Radiology 247(2):472–481PubMed

25.

Parfitt AM, Drezner MK, Glorieux FH, Kanis JA, Malluche H, Meunier PJ, Ott SM, Recker RR (1987) Bone histomorphometry: standardization of nomenclature, symbols, and units. Report of the ASBMR Histomorphometry Nomenclature Committee. J Bone Miner Res 2(6):595–610CrossRefPubMed

26.

Saha PK, Wehrli FW (2004) Measurement of trabecular bone thickness in the limited resolution regime of in vivo MRI by fuzzy distance transform. IEEE Trans Med Imaging 23(1):53–62CrossRefPubMed

27.

Michielsen K, de Raedt H (2001) Integral-geometry morphological image analysis. Phys Rep 347:461–583CrossRef

28.

Eckstein F, Wunderer C, Boehm H, Kuhn V, Priemel M, Link TM, Lochmuller EM (2004) Reproducibility and side differences of mechanical tests for determining the structural strength of the proximal femur. J Bone Miner Res 19(3):379–385CrossRefPubMed

29.

Gluer CC, Blake G, Lu Y, Blunt BA, Jergas M, Genant HK (1995) Accurate assessment of precision errors: how to measure the reproducibility of bone densitometry techniques. Osteoporos Int 5(4):262–270CrossRefPubMed

30.

Wehrli FW, Gomberg BR, Saha PK, Song HK, Hwang SN, Snyder PJ (2001) Digital topological analysis of in vivo magnetic resonance microimages of trabecular bone reveals structural implications of osteoporosis. J Bone Miner Res 16(8):1520–1531CrossRefPubMed

31.

Wehrli FW, Ladinsky GA, Jones C, Benito M, Magland J, Vasilic B, Popescu AM, Zemel B, Cucchiara AJ, Wright AC, Song HK, Saha PK, Peachey H, Snyder PJ (2008) In vivo magnetic resonance detects rapid remodeling changes in the topology of the trabecular bone network after menopause and the protective effect of estradiol. J Bone Miner Res 23(5):730–740CrossRefPubMed

32.

Eckstein F, Lochmuller EM, Lill CA, Kuhn V, Schneider E, Delling G, Muller R (2002) Bone strength at clinically relevant sites displays substantial heterogeneity and is best predicted from site-specific bone densitometry. J Bone Miner Res 17(1):162–171CrossRefPubMed

33.

Lochmuller EM, Muller R, Kuhn V, Lill CA, Eckstein F (2003) Can novel clinical densitometric techniques replace or improve DXA in predicting bone strength in osteoporosis at the hip and other skeletal sites? J Bone Miner Res 18(5):906–912CrossRefPubMed

34.

Augat P, Schorlemmer S (2006) The role of cortical bone and its microstructure in bone strength. Age Ageing 35(Suppl 2):ii27–ii31CrossRefPubMed

35.

Manske SL, Liu-Ambrose T, Cooper DM, Kontulainen S, Guy P, Forster BB, McKay HA (2008) Cortical and trabecular bone in the femoral neck both contribute to proximal femur failure load prediction. Osteoporos Int 20(3):445–453CrossRefPubMed

36.

Wachter NJ, Krischak GD, Mentzel M, Sarkar MR, Ebinger T, Kinzl L, Claes L, Augat P (2002) Correlation of bone mineral density with strength and microstructural parameters of cortical bone in vitro. Bone 31(1):90–95CrossRefPubMed

37.

Manske SL, Liu-Ambrose T, de Bakker PM, Liu D, Kontulainen S, Guy P, Oxland TR, McKay HA (2006) Femoral neck cortical geometry measured with magnetic resonance imaging is associated with proximal femur strength. Osteoporos Int 17(10):1539–1545CrossRefPubMed

38.

Bessho M, Ohnishi I, Okazaki H, Sato W, Kominami H, Matsunaga S, Nakamura K (2004) Prediction of the strength and fracture location of the femoral neck by CT-based finite-element method: a preliminary study on patients with hip fracture. J Orthop Sci 9(6):545–550CrossRefPubMed

39.

Bessho M, Ohnishi I, Matsuyama J, Matsumoto T, Imai K, Nakamura K (2007) Prediction of strength and strain of the proximal femur by a CT-based finite element method. J Biomech 40(8):1745–1753CrossRefPubMed

40.

Keyak JH, Falkinstein Y (2003) Comparison of in situ and in vitro CT scan-based finite element model predictions of proximal femoral fracture load. Med Eng Phys 25(9):781–787CrossRefPubMed

41.

Yosibash Z, Trabelsi N, Milgrom C (2007) Reliable simulations of the human proximal femur by high-order finite element analysis validated by experimental observations. J Biomech 40(16):3688–3699CrossRefPubMed

42.

Yosibash Z, Padan R, Joskowicz L, Milgrom C (2007) A CT-based high-order finite element analysis of the human proximal femur compared to in-vitro experiments. J Biomech Eng 129(3):297–309CrossRefPubMed

43.

Li W, Sode M, Saeed I, Lang T (2006) Automated registration of hip and spine for longitudinal QCT studies: integration with 3D densitometric and structural analysis. Bone 38(2):273–279CrossRefPubMed

44.

Saparin P, Thomsen JS, Kurths J, Beller G, Gowin W (2006) Segmentation of bone CT images and assessment of bone structure using measures of complexity. Med Phys 33(10):3857–3873CrossRefPubMed

45.

Dontas IA, Yiannakopoulos CK (2007) Risk factors and prevention of osteoporosis-related fractures. J Musculoskelet Neuronal Interact 7(3):268–272PubMed

Title: Automated 3D trabecular bone structure analysis of the proximal femur—prediction of biomechanical strength by CT and DXA
Authors: T. Baum
J. Carballido-Gamio
M. B. Huber
D. Müller
R. Monetti
C. Räth
F. Eckstein
E. M. Lochmüller
S. Majumdar
E. J. Rummeny
T. M. Link
J. S. Bauer
Publication date: 01-09-2010
Publisher: Springer-Verlag
Published in: Osteoporosis International / Issue 9/2010
Print ISSN: 0937-941X
Electronic ISSN: 1433-2965
DOI: https://doi.org/10.1007/s00198-009-1090-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Automated 3D trabecular bone structure analysis of the proximal femur—prediction of biomechanical strength by CT and DXA

Abstract

Summary

Introduction

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Summary

Introduction

Methods

Results

Conclusion

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