Biomedical subjects

Fulvia Taddei

Publications and source records attributed to Fulvia Taddei.

8 recordsLinked to original sources

PubMed2006

The material mapping strategy influences the accuracy of CT-based finite element models of bones: an evaluation against experimental measurements.

Aim of the present study was to evaluate the influence on the global model's accuracy of the strategy adopted to define the average element Young's modulus in subject-specific finite element models of bones from computed tomography data. The classic strategy of calculating the Young's modulus from an average element density and the one that averages the Young's moduli directly derived from each voxel Hounsfield Unit were considered. These strategies were applied to the finite element model of a real human femur. The accuracy of the superficial stress and strain predictions was evaluated against experimentally measured values in 13 strain-gauge locations for five different loading conditions. The results obtained for the two material distributions were statistically different. Both models predicted very accurately the superficial stresses, with regression coefficients higher than 0.9 and slopes not significantly different from unity. The second strategy definitely improved the strains prediction accuracy: the regression coefficient raised from 0.69 to 0.79; the average and peak errors decreased from 45.1% to 31.3% and from 228% to 134% of the maximum measured strain, respectively. The stress fields predicted inside the bone were also significantly different. A new software implementing the second strategy was made available in the public domain.

Bone Density↗

PubMed2006

The multimod application framework: a rapid application development tool for computer aided medicine.

This paper describes a new application framework (OpenMAF) for rapid development of multimodal applications in computer-aided medicine. MAF applications are multimodal in data, in representation, and in interaction. The framework supports almost any type of biomedical data, including DICOM datasets, motion-capture recordings, or data from computer simulations (e.g. finite element modeling). The interactive visualization approach (multimodal display) helps the user interpret complex datasets, providing multiple representations of the same data. In addition, the framework allows multimodal interaction by supporting the simultaneous use of different input-output devices like 3D trackers, stereoscopic displays, haptics hardware and speech recognition/synthesis systems. The Framework has been designed to run smoothly even on limited power computers, but it can take advantage of all hardware capabilities. The Framework is based on a collection of portable libraries and it can be compiled on any platform that supports OpenGL, including Windows, MacOS X and any flavor of Unix/linux.

Humans↗

PubMed2006

Finite-element modeling of bones from CT data: sensitivity to geometry and material uncertainties.

The aim of this paper is to analyze how the uncertainties in modelling the geometry and the material properties of a human bone affect the predictions of a finite-element model derived from computed tomography (CT) data. A sensitivity analysis, based on a Monte Carlo method, was performed using three femur models generated from in vivo CT datasets, each subjected to two different loading conditions. The geometry, the density and the mechanical properties of the bone tissue were considered as random input variables. Finite-element results typically used in biomechanics research were considered as statistical output variables, and their sensitivity to the inputs variability assessed. The results showed that it is not possible to define a priori the influence of the errors related to the geometry definition process and to the material assignment process on the finite-element analysis results. The errors in the geometric representation of the bone are always the dominant variables for the stresses, as was expected. However, for all the variables, the results seemed to be dependent on the loading condition and to vary from subject to subject. The most interesting result is, however, that using the proposed method to build a finite-element model of a femur from a CT dataset of the quality typically achievable in the clinical practice, the coefficients of variation of the output variables never exceed the 9%. The presented method is hence robust enough to be used for investigating the mechanical behavior of bones with subject-specific finite-element models derived from CT data taken in vivo.

Computer Simulation↗

PubMed2005

Subject-specific finite element models of long bones: An in vitro evaluation of the overall accuracy.

The determination of the mechanical stresses induced in human bones is of great importance in both research and clinical practice. Since the stresses in bones cannot be measured non-invasively in vivo, the only way to estimate them is through subject-specific finite element modelling. Several methods exist for the automatic generation of these models from CT data, but before bringing them in the clinical practice it is necessary to assess their accuracy in the predictions of the bone stresses. Particular attention should be paid to those regions, like the epiphyseal and metaphyseal parts of long bones, where the automatic methods are typically less accurate. Aim of the present study was to implement a general procedure to automatically generate subject-specific finite element models of bones from CT data and estimate the accuracy of this general procedure by applying it to one real femur. This femur was tested in vitro under five different loading scenarios and the results of these tests were used to verify how the adoption of a simplified two-material homogeneous model would change the accuracy with respect to the density-based inhomogeneous one, with special attention paid to the epiphyseal and metaphyseal proximal regions of the bone. The results showed that the density-based inhomogeneous model predicts with a very good accuracy the measured stresses (R(2)=0.91, RMSE=8.6%, peak error=27%), while the two-material model was less accurate (R(2)=0.89, RMSE=9.6%, peak error=35%). The results showed that it is possible to automatically generate accurate finite element models of bones from CT data and that the strategy of material properties mapping has a significant influence on its accuracy.

Femur↗

PubMed2004

New aspects and approaches in pre-operative planning of hip reconstruction: a computer simulation.

BACKGROUND: All computer-aided surgery technologies assume that the surgeon knows the best position for the implant components. However, there is indirect evidence that simple anatomical information may not be sufficient for the surgeon to decide size and position of the implant in a repeatable manner. METHOD: In the present study we estimated the variability in choosing the size and position of the components of a cementless total hip replacement (THR), using template-on-radiograph as well as computed tomography (CT)-based computer-aided planning. In addition, using a computer model, we assessed the sensitivity to such variability of implant fitting, location of the joint centre, skeletal range of motion, and resting length of major hip muscles. Using templates, surgeons selected the size with variability up to 2.5 mm for the stem and up to 4 mm for the socket. A similar variability was also observed when the CT-based planning program was used. RESULTS: No major differences were found between surgeons. The standard deviation over repeated planning sessions of the selected position for each component, using the CT-based planning software, was found to be 3.9 mm and 8.9 degrees . CONCLUSION: On the basis of the computer simulation, this variability did not affect the selected biomechanical parameters in a drastic way, although some differences were observed, especially in the lever arm of the hip muscles.

Arthroplasty, Replacement, Hip↗

PubMed2004

Automatic generation of accurate subject-specific bone finite element models to be used in clinical studies.

Most of the finite element models of bones used in orthopaedic biomechanics research are based on generic anatomies. However, in many cases it would be useful to generate from CT data a separate finite element model for each subject of a study group. In a recent study a hexahedral mesh generator based on a grid projection algorithm was found very effective in terms of accuracy and automation. However, so far the use of this method has been documented only on data collected in vitro and only for long bones. The present study was aimed at verifying if this method represents a procedure for the generation of finite element models of human bones from data collected in vivo, robust, accurate, automatic and general enough to be used in clinical studies. Robustness, automation and numerical accuracy of the proposed method were assessed on five femoral CT data sets of patients affected by various pathologies. The generality of the method was verified by processing a femur, an ileum, a phalanx, a proximal femur reconstruction, and the micro-CT of a small sample of spongy bone. The method was found robust enough to cope with the variability of the five femurs, producing meshes with a numerical accuracy and a computational weight comparable to those found in vitro. Even when the method was used to process the other bones the levels of mesh conditioning remained within acceptable limits. Thus, it may be concluded that the method presents a generality sufficient to cope with almost any orthopaedic application.

Adult↗

PubMed2004

An improved method for the automatic mapping of computed tomography numbers onto finite element models.

The assignment of bone tissue material properties is a fundamental step in the generation of subject-specific finite element models from computed tomography data. Aim of the present work is to investigate the influence of the material mapping algorithm on the results predicted by the finite element analysis. Two models, a coarse and a refined one, of a human ileum, femur and tibia, were generated from CT data and used for the tests. In addition a convergence analysis was carried out for the femur model, using six refinement levels, to verify whether the inclusion of the material properties would significantly alter the convergence behaviour of the mesh. The results showed that the choice of the mapping algorithm influences the material distribution. However, this did not always propagate into the finite element results. The difference between the maximum Von Mises stress remained always lower than 10%, apart one case when it reached the 13%. However, the global behaviour of the meshes showed more marked differences between the two algorithms: in the finer meshes of the two long bones 20-30% of the bone volume showed differences in the predicted Von Mises stresses greater than 10%. The convergence behaviour of the model was not worsened by the introduction of inhomogeneous material properties. The software was made available in the public domain.

Absorptiometry, Photon↗

PubMed2003

Automatic generation of finite element meshes from computed tomography data.

A major obstacle for a broader adoption of the finite element method (FEM) in clinical biomedical applications is the generation of the model, frequently too slow for the times imposed by the clinical practice. The algorithms for automatic mesh generation have greatly improved, but their adoption by the biomedical community is still limited. The aim of this work is to review the principal algorithms for automatic mesh generation and to critically discuss them with particular reference to their applicability in the biomedical field. Specialized literature on numerical methods was reviewed in order to identify the main theoretical approaches currently available for automatic mesh generation. Then, published methods for the automatic generation of finite element models of organs from computed tomography data were reviewed and classified with a proposed taxonomy. Each method was reconnected to a theoretical approach described in the specialized literature whenever possible. Last, each method was critically reviewed with respect to its applicability to the clinical practice. None of the methods described satisfy all the requirements in terms of automation, generality, accuracy, and robustness imposed by a clinical application. However, some of these methods can already be successfully used in various application contexts, and a few guidelines are drawn.

Algorithms↗