Noninvasive prediction of vertebral body compressive strength using nonlinear finite element method and an image based technique.
Noninvasive prediction of vertebral body strength under compressive loading condition is a valuable tool for the assessment of clinical fractures. This paper presents an effective specimen-specific approach for noninvasive prediction of human vertebral strength using a nonlinear finite element (FE) model and an image based parameter based on the quantitative computed tomography (QCT). Nine thoracolumbar vertebrae excised from three cadavers with an average age of 42 years old were used as the samples. The samples were scanned using the QCT. Then, a segmentation technique was performed on each QCT sectional image. The segmented images were then converted into three-dimensional FE models for linear and nonlinear analyses. A new material model was implemented in our nonlinear model being more compatible with real mechanical behavior of trabecular bone. A new image based MOS (Mechanic of Solids) parameter named minimum sectional strength ((sigma(u)A)(min)) was used for the ultimate compressive strength prediction. Subsequently, the samples were destructively tested under uniaxial compression and their experimental ultimate compressive strengths were obtained. Results indicated that our new implemented FE model can predict ultimate compressive strength of human vertebra with a correlation coefficient (R(2)=0.94) better than usual linear and nonlinear FE models (R(2)=0.83 and 0.85 respectively). The image based parameter introduced in this study ((sigma(u)A)(min)) was also correlated well with the experimental results (R(2)=0.86). Although nonlinear FE method with new implemented material model predicts compressive strength better than the (sigma(u)A)(min), this parameter is clinically more feasible due to its simplicity and lower computational costs. This can make future applications of the (sigma(u)A)(min) more justified for human vertebral body compressive strength prediction.
Zeinali
Ahad
A
Department of Medical Imaging, Urmia Medical Sciences University, Urmia, Iran.
Hashemi
Bijan
B
Akhlaghpoor
Shahram
S
D000328:Adult; D000465:Algorithms; D019245:Compressive Strength; D004548:Elasticity; D020342:Finite Element Analysis; D006801:Humans; D066298:In Vitro Techniques; D016014:Linear Models; D008159:Lumbar Vertebrae / Q000000981:diagnostic imaging / Q000502:physiology*; D008954:Models, Biological; D017711:Nonlinear Dynamics; D019047:Phantoms, Imaging; D013904:Thoracic Vertebrae / Q000000981:diagnostic imaging / Q000502:physiology*; D014057:Tomography, X-Ray Computed / Q000295:instrumentation