Functional Imaging of the Proximal Femur Using Shape Template and a Bone Mineral Density Image (original) (raw)
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Computer Methods in Biomechanics and Biomedical Engineering, 2012
Skeletal fractures associated with bone mass loss are a major clinical problem and economic burden, and lead to significant morbidity and mortality in the aging population. Clinical image based measures of bone mass show only moderate correlative strength with bone strength. However, engineering models derived from clinical image data predict bone strength with significantly greater accuracy. Currently, image-based finite element (FE) models are time consuming to construct and are non parametric. The goal of this study was to develop a parametric proximal femur FE model based on a statistical shape and density model (SSDM) derived from clinical image data. A small number of independent SSDM parameters described the shape and bone density distribution of a set of cadaver femurs and captured the variability affecting proximal femur FE strength predictions. Finally, a 3D FE model of an "unknown" femur was reconstructed from the SSDM with an average spatial error of 0.016 mm and an average bone density error of 0.037 g/cm 3 .
Osteoporosis International, 2010
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.
Computer Methods and Programs in Biomedicine, 2011
Two dimensional finite element models of cadaveric femoral stiffness were developed to study their suitability as surrogates of bone stiffness and strength, using two dimensional representations of femoral geometry and bone mineral density distributions. If successfully validated, such methods could be clinically applied to estimate patient bone stiffness and strength using simpler and less costly radiographs. Two dimensional femur images were derived by projection of quantitative computed tomography scans of 22 human cadaveric femurs. The same femurs were fractured in a fall on the hip configuration. Femoral stiffness and fracture load were measured, and high speed video was recorded. Digital image correlation analysis was used to calculate the strain distribution from the high speed video recordings. Two-dimensional projection images were segmented and meshed with second-order triangular elements for finite element analysis. Elastic moduli of the finite elements were calculated based on the projected mineral density values inside the elements. The mapping of projection density values to elastic modulus was obtained using optimal parameter identification in a set of nine of the 22 specimens, and validated on the remaining 13 specimens. Finite element calculated proximal stiffness and strength correlated much better with experimental data than areal bone mineral density alone. In addition, finite element calculated strain distributions compared very well with strains obtained from digital image processing of the high speed video recordings, further validating the two-dimensional projected subject-specific finite element models.
Journal of Clinical Densitometry, 2017
Structural parameters of the proximal femur evaluate the strength of the bone and its susceptibility to fracture. These parameters are computed from dual-energy X-ray absorptiometry (DXA) or from quantitative computed tomography (QCT). The 3-dimensional (3D)-DXA software solution provides 3D models of the proximal femur shape and bone density from anteroposterior DXA scans. In this paper, we present and evaluate a new approach to compute structural parameters using 3D-DXA software. A cohort of 60 study subjects (60.9 ± 14.7 yr) with DXA and QCT examinations was collected. 3D femoral models obtained by QCT and 3D-DXA software were aligned using rigid registration techniques for comparison purposes. Geometric, cross-sectional, and volumetric structural parameters were computed at the narrow neck, intertrochanteric, and lower shaft regions for both QCT and 3D-DXA models.The accuracy of 3D-DXA structural parameters was evaluated in comparison with QCT. Correlation coefficients (r) between geometric parameters computed by QCT and 3D-DXA software were 0.86 for the femoral neck axis length and 0.71 for the femoral neck shaft angle. Correlation coefficients ranged from 0.86 to 0.96 for the cross-sectional parameters and from 0.84 to 0.97 for the volumetric structural parameters. Our study demonstrated that accurate estimates of structural parameters for the femur can be obtained from 3D-DXA models. This provides clinicians with 3D indexes related to the femoral strength from routine anteroposterior DXA scans, which could potentially improve osteoporosis management and fracture prevention.
2010
Area Bone Mineral Density (aBMD) measured by Dual energy X-ray Absorptiometry (DXA) is an established criterion in the evaluation of hip fracture risk. The evaluation from these planar images however is limited to 2D while it has been shown that proper 3D assessment of both the shape and the BMD distribution improves the fracture risk estimation. In this work we present a method to reconstruct both the 3D bone shape and 3D Bone Mineral Density (BMD) distribution of the proximal femur from a single DXA image. A statistical model of shape and a separate statistical model of the BMD was automatically constructed from a set of quantitative computed tomography (QCT) scans. The reconstruction method incorporates a fully automatic intensity based 3D-2D registration process, maximizing the similarity between the DXA and a digitally reconstructed radiograph of the combined model. For the experiments an in vitro dataset of QCT scans of 90 anatomical specimens was used. Out of these, 60 were used to construct both the shape and the density model. To evaluate the reconstruction accuracy, experiments were performed on simulated DXA images from the remaining 30 QCT scans. Comparisons between the reconstructions from DXA with the same subject QCT scans showed a mean shape accuracy of 1.2mm whereby 95% of the error is below 3.2mm, and a mean density error of 81mg/cm 3 corresponding to 4.6% of the whole range of bone density values. The results show that this method is capable of accurately reconstructing both the 3D shape and 3D BMD distribution of the proximal femur from DXA images used in clinical routine, potentially improving the diagnosis of osteoporosis and fracture risk assessments at a low radiation dose and low cost.
Medical Imaging 2010: Image Processing, 2010
Area Bone Mineral Density (aBMD) measured by Dual energy X-ray Absorptiometry (DXA) is an established criterion in the evaluation of hip fracture risk. The evaluation from these planar images however is limited to 2D while it has been shown that proper 3D assessment of both the shape and the BMD distribution improves the fracture risk estimation. In this work we present a method to reconstruct both the 3D bone shape and 3D Bone Mineral Density (BMD) distribution of the proximal femur from a single DXA image. A statistical model of shape and a separate statistical model of the BMD was automatically constructed from a set of quantitative computed tomography (QCT) scans. The reconstruction method incorporates a fully automatic intensity based 3D-2D registration process, maximizing the similarity between the DXA and a digitally reconstructed radiograph of the combined model. For the experiments an in vitro dataset of QCT scans of 90 anatomical specimens was used. Out of these, 60 were used to construct both the shape and the density model. To evaluate the reconstruction accuracy, experiments were performed on simulated DXA images from the remaining 30 QCT scans. Comparisons between the reconstructions from DXA with the same subject QCT scans showed a mean shape accuracy of 1.2mm whereby 95% of the error is below 3.2mm, and a mean density error of 81mg/cm 3 corresponding to 4.6% of the whole range of bone density values. The results show that this method is capable of accurately reconstructing both the 3D shape and 3D BMD distribution of the proximal femur from DXA images used in clinical routine, potentially improving the diagnosis of osteoporosis and fracture risk assessments at a low radiation dose and low cost.
Medical Image Analysis, 2015
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights 3D shape, vBMD and FE mesh of the proximal femur were reconstructed from a DXA image Surface reconstruction error was 1.0-1.4mm Use of DXA, instead of projected CT images, had no significant effect on the reconstruction accuracy DXA-based FE agreed well with CT-based FE analysis (stiffness r 2 =0.85, MAC=0. 0.977) DXA-based FE analysis may help to simulate patient-specific bone mechanics
IEEE Transactions on Medical Imaging, 2000
The accurate diagnosis of osteoporosis has gained increasing importance due to the aging of our society. Areal bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is an established criterion in the diagnosis of osteoporosis. This measure, however, is limited by its two-dimensionality. This work presents a method to reconstruct both the 3D bone shape and 3D BMD distribution of the proximal femur from a single DXA image used in clinical routine. A statistical model of the combined shape and BMD distribution is presented, together with a method for its construction from a set of quantitative computed tomography (QCT) scans. A reconstruction is acquired in an intensity based 3D-2D registration process whereby an instance of the model is found that maximizes the similarity between its projection and the DXA image. Reconstruction experiments were performed on the DXA images of 30 subjects, with a model constructed from a database of QCT scans of 85 subjects. The accuracy was evaluated by comparing the reconstructions with the same subject QCT scans. The method presented here can potentially improve the diagnosis of osteoporosis and fracture risk assessment from the low radiation dose and low cost DXA devices currently used in clinical routine.
An emerging focus on the investigation and analysis of the biomechanics of human bone is to generate a preclinical information which is helpful for the researcher and orthopedicians has been seen. For this, a geometric model that acts like a natural bone have increasingly been considered to better understand the mechanics of the bone. Hip joint is one of the most important joints in the human body. It is formed by the articulation of femur and acetabulum of the pelvis. It allows us to walk, run, and jump. It bears our body's weight and the force of the strong muscles of the hip and leg. So, aim of this study is to reconstruct appropriate three-dimensional (3D) computer aided design (CAD) of femur for prediction of stress transfer after total hip replacement (THR). A 3D finite element model of femur was developed based on computed tomography (CT), DICOM images and finally a developed finite element (FE) model was analyzed under physiological load conditions. The results of the analysis are helpful for the orthopedic surgeon to understand the mechanical behavior of the femur bone and in hip replacement surgeries and implant fixation.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2011
This work presents a statistical model of both the shape and Bone Mineral Density (BMD) distribution of the proximal femur for fracture risk assessment. The shape and density model was built from a dataset of Quantitative Computed Tomography scans of fracture patients and a control group. Principal Component Analysis and Horn's parallel analysis were used to reduce the dimensionality of the shape and density model to the main modes of variation. The input data was then used to analyze the model parameters for the optimal separation between the fracture and control group. Feature selection using the Fisher criterion determined the parameters with the best class separation, which were used in Fisher Linear Discriminant Analysis to find the direction in the parameter space that best separates the fracture and control group. This resulted in a Fisher criterion value of 6.70, while analyzing the Dual-energy X-ray Absorptiometry derived femur neck areal BMD of the same subjects result...