Densitometry test of bone tissue: Validation of computer simulation studies

Bone densitometry measurements are performed to predict the fracture risk in bones. However, the sensitivity of these predictions are not satisfactory. One of the explanations is that densitometry ignores the (architectural) structural aspects of the bone. The effects of varying architectural parameters on the densitometry parameters can be effectively assessed by considering a 3-D image of a bone and vary the bone structure parameters in a controlled manner and determine the consequence of these changes on a simulated (virtual) densitometry analysis. In this paper we present such a computer simulation of densitometry analysis of bone. The simulation allows quantification of densitometry parameters, such as BMD and BMC, on the basis of computed tomography bone scans. The aim of the presented study is the evaluation of our method by comparing its results to the results from real densitometry (DEXA) tests. For the evaluation we selected three femoral bones. These items were CT scanned and individual computer models were created. In addition, the densitometry parameters of these items were assessed by a clinical DEXA scanner. The densitometry parameters obtained from the simulations were very close to the results from the DEXA densitometry measurements. We therefore conclude that our method can be employed in the research on the influence of changes in bone structure on densitometry test results.

[1]  Harold L. Kundel,et al.  Handbook of Medical Imaging, Volume 1. Physics and Psychophysics , 2000 .

[2]  Jamshid Tehranzadeh,et al.  Predicting the Strength of Femoral Shafts with and without Metastatic Lesions , 2005, Clinical orthopaedics and related research.

[3]  A. Silman,et al.  Predictive Value of BMD for Hip and Other Fractures , 2005, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[4]  Nicolas Pallikarakis,et al.  A software data generator for radiographic imaging investigations , 2000, IEEE Transactions on Information Technology in Biomedicine.

[5]  Richard L. Van Metter,et al.  Handbook of Medical Imaging , 2009 .

[6]  O Johnell,et al.  Risk of hip fracture according to the World Health Organization criteria for osteopenia and osteoporosis. , 2000, Bone.

[7]  Zygmunt Wróbel,et al.  The analysis of densitometry image of bone tissue based on computer simulation of X-ray radiation propagation through plate model , 2007, Comput. Biol. Medicine.

[8]  H.W.J. Huiskes,et al.  Basic orthopaedic biomechanics and mechano-biology , 2005 .

[9]  S. Cowin Bone mechanics handbook , 2001 .

[10]  C. Langton,et al.  The Physical Measurement of Bone , 2003 .

[11]  K Bliznakova,et al.  A three-dimensional breast software phantom for mammography simulation. , 2003, Physics in medicine and biology.

[12]  Nicolas Pallikarakis,et al.  Monte Carlo Simulation of the Radiographic Imaging Procedure for Electronically Designed Phantoms , 1999, MIE.

[13]  G J Michael,et al.  Simulation studies of optimum energies for DXA: dependence on tissue type, patient size and dose model. , 1999, Australasian physical & engineering sciences in medicine.

[14]  Niklas Zethraeus,et al.  Intervention thresholds for osteoporosis in the UK. , 2005, Bone.

[15]  A Hofman,et al.  Risk factors for increased bone loss in an elderly population: the Rotterdam Study. , 1998, American journal of epidemiology.

[16]  Kristina Bliznakova,et al.  An integrated research tool for X-ray imaging simulation , 2003, Comput. Methods Programs Biomed..

[17]  J H Keyak,et al.  Young-elderly differences in bone density, geometry and strength indices depend on proximal femur sub-region: a cross sectional study in Caucasian-American women. , 2006, Bone.

[18]  K Bliznakova,et al.  Dual-energy mammography: simulation studies. , 2006, Physics in medicine and biology.

[19]  G. Michael,et al.  Monte Carlo modelling of an extended DXA technique. , 1998, Physics in medicine and biology.

[20]  J. Tehranzadeh,et al.  Mechanical properties, density and quantitative CT scan data of trabecular bone with and without metastases. , 2004, Journal of biomechanics.

[21]  K Bliznakova,et al.  Integrated software system for improving medical equipment management. , 2003, Biomedical instrumentation & technology.

[22]  H. Kroger,et al.  The use of multiple sites for the diagnosis of osteoporosis , 2006, Osteoporosis International.

[23]  J H Keyak,et al.  Prediction of fracture location in the proximal femur using finite element models. , 2001, Medical engineering & physics.

[24]  J H Keyak,et al.  Stiff and strong compressive properties are associated with brittle post‐yield behavior in equine compact bone material , 2002, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[25]  Jamshid Tehranzadeh,et al.  Predicting Proximal Femoral Strength Using Structural Engineering Models , 2005, Clinical orthopaedics and related research.

[26]  O. Johnell,et al.  Fracture risk following an osteoporotic fracture , 2004, Osteoporosis International.

[27]  J. H. Hubbell,et al.  Tables of X-Ray Mass Attenuation Coefficients and Mass Energy-Absorption Coefficients 1 keV to 20 MeV for Elements Z = 1 to 92 and 48 Additional Substances of Dosimetric Interest , 1995 .

[28]  J. Tehranzadeh,et al.  Relationships between material properties and CT scan data of cortical bone with and without metastatic lesions. , 2003, Medical engineering & physics.