Patient-Specific Finite Element Models of Posterior Pedicle Screw Fixation: Effect of Screw's Size and Geometry

Pedicle screw fixation is extensively performed to treat spine injuries or diseases and it is common for thoracolumbar fractures. Post-operative complications may arise from this surgery leading to back pain or revisions. Finite element (FE) models could be used to predict the outcomes of surgeries but should be verified when both simplified and realistic designs of screws are used. The aim of this study was to generate patient-specific Computed Tomography (CT)-based FE models of human vertebrae with two pedicle screws, verify the models, and use them to evaluate the effect of the screws’ size and geometry on the mechanical properties of the screws-vertebra structure. FE models of the lumbar vertebra implanted with two pedicle screws were created from anonymized CT-scans of three patients. Compressive loads were applied to the head of the screws. The mesh size was optimized for realistic and simplified geometry of the screws with a mesh refinement study. Finally, the optimal mesh size was used to evaluate the sensitivity of the model to changes in screw’s size (diameter and length) and geometry (realistic or simplified). For both simplified and realistic models, element sizes of 0.6 mm in the screw and 1.0 mm in the bone allowed to obtain relative differences of approximately 5% or lower. Changes in screw’s length resulted in 4–10% differences in maximum deflection, 1–6% differences in peak stress in the screws, 10–22% differences in mean strain in the bone around the screw; changes in screw’s diameter resulted in 28–36% differences in maximum deflection, 6–27% differences in peak stress in the screws, and 30–47% differences in mean strain in the bone around the screw. The maximum deflection predicted with realistic or simplified screws correlated very well (R2 = 0.99). The peak stress in screws with realistic or simplified design correlated well (R2 = 0.82) but simplified models underestimated the peak stress. In conclusion, the results showed that the diameter of the screw has a major role on the mechanics of the screw-vertebral structure for each patient. Simplified screws can be used to estimate the mechanical properties of the implanted vertebrae, but the systematic underestimation of the peak stress should be considered when interpreting the results from the FE analyses

Computational models for the pre-operative planning of spinal surgeries

Spinal surgeries are common to treat different types of diseases and injuries as trauma, tumours, deformity and degenerative diseases. Conventional open spine surgery has several reported limitations and there is a trend towards minimally invasive techniques due to lower complication rates and morbidity. Percutaneous pedicle screw fixation and vertebral augmentation are two widespread minimally invasive techniques that are often chosen to treat vertebral fractures. The pre-planning of spinal surgeries is based on anatomical measurements taken on clinical images and on the experience of surgeons. Post-operative complications may arise impacting the quality of life of patients. Computational models can provide important patient-specific information about the biomechanics and the geometry of the spine. Finite element (FE) models have the potential to predict the biomechanical outcomes of surgeries and are often proposed as possible tools for planning pedicle screw fixation. However, before their application, these models have to be verified and the sensitivity of metrics used to assess metal and bone failure have to be assessed with respect to the screw size and geometry. This was the aim of the first study. Patient-specific Computed Tomography (CT)-based FE models of the human vertebra with two pedicle screws were verified for both realistic and simplified geometry of screws. The diameter of the screw played a major role on the mechanics of the screw-vertebral structure with respect to the length. Simplified screws could accurately estimate the deflection and the strain of the implanted vertebrae, but resulted in a systematic underestimation of the peak stress in the screws. FE models can be used to optimize surgery-related parameters, but take a long time to compute and are thus insufficient to fulfil the demands of most clinical settings. Reduced Order Models (ROMs) are useful tools to improve the efficiency of FE models, and, in this thesis, were applied to FE models of the implanted vertebra to optimise screws’ size and orientation showing accurate prediction of the deflection and the stress of the screws. A third study included the development of a CT-scan based procedure to estimate the pre-fracture 3D shape of a L1 vertebra that could be used by surgeons to restore the pre-fracture biomechanics. The methodology was validated on a dataset with 40 patients and showed excellent reconstruction accuracy. In conclusion, FE models of the implanted vertebra were integrated with ROMs to build a computational pipeline for the optimisation of dimensions and positioning of pedicle screws. Also, the geometric pre-fracture shape of a L1 vertebra was reconstructed. These approaches can be used to provide more quantitative biomechanical and geometric information to surgeons for planning the treatment of vertebral fractures

Data and codes for the paper: "Prediction of the 3D shape of the L1 vertebral body from adjacent vertebrae"

Data and codes used in the paper: "Prediction of the 3D shape of the L1 vertebral body from adjacent vertebrae" by M.Sensale, T. Vendeuvre, A. Germaneau, C. Grivot, M. Rochette, E. Dall‘Ara" published in Medical Image Analysis We share here the results as inputs and outputs of the model. Parts of the model are proprietary of ANSYS and cannot be shared.  The dataset includes: Segmentations of the vertebral bodies Mesh smoothed Mesh aligned Landmarks Reference mesh Morphed meshes Code to make mesh morphing Code to make prediction of shape of L1 In case the reader is interested in the whole database they should contact Dr Enrico Dall'Ara ([email protected]).</p

From experimental data to a numerical model of Keloid-Skin Composite structure

International audienceAs keloids are a consequence of abnormal wound healing process, the fibroblast proliferate excessively in dermis (Ogawa & Orgill, Mechanobiology of Cutaneous Wound Healing and Scarring, in Bioengineering Research of Chronic Wounds, 2009) and develop an unsightly and uncomfortable tumor that replaces the healthy skin. The structure and the properties of keloids highly differe from healthy skin, in particular the mechanical properties. Several anatomic sites are known as pro-keloid sites. Others don’t develop any keloid (Ogawa & Orgill, Mechanobiology of Cutaneous Wound Healing and Scarring, in Bioengineering Research of Chronic Wounds, 2009). In addition to genetic and biological causes, the mechanical solicitation of the in vivo skin induces the growth of keloid. Furthermore, this is highly related to the site and the range of body motion. This assumption is well documented by (Ogawa, et al., 2012). The design of a medical device should improve the prevention of the pathology but the mechanical components of the stress fields have to be determine in order to explain the mechanical process of keloid growth

Mechanical parameters identification of keloid and surrounding healthy skin using Digital Image Correlation measurements in vivo

The human skin behaves as an elastic membrane initially prestressed but not uniformly. The presence of anatomical sites favorable to the appearance of some tumors, a keloid in our case, while other sites never develop them attests to the importance of the mechanical environment of the tissue. Thus, a mechanical characterization of the tumored skin is necessary to understand the keloid expansion from a mechanical point of view. Our case study consists in modeling a bi-material structure composed of a keloid skin surrounded by healthy skin located on upper left arm of a young female. From the experimental measurements in vivo, by combining force sensor, displacement sensor and Digital Image Correlation techniques, we perform a mechanical analysis to characterize the mechanical stress fields over the entire area and on the interface ‘healthy skin/keloid skin’. Since the mechanical behavior of the tumorous skin is unknown, many physical models can be implemented and assessed very easily inside the specific digital software to fit with the real data. Once a set of mechanical parameters for both the healthy skin and the keloid skin are identified, the stress fields around the keloid are calculated. Next steps consist in determining matching preferential directions in order to define as precisely as possible the specifications of a device for preventing the growth of keloids

Parameter identification problem in bimaterial human skin and sensitivity analysis : Uncertainties in biomechanics of skin

The proposed paper concerns the prediction of the numerical response of a biomechanical structure submitted to an unknown external loading state. The methodology is based on homogeneous and then heterogeneous structures such as healthy or pathological cutaneous tissues that can be mechanically tested in vivo under a patchy knowledge of boundary conditions. Experimental data corresponding to the extension of a piece of skin located between two pads with displacement enslavement, represent input data to the numerical model. Data are reaction force on one pad and displacement field between the two pads and all around. The numerical model consists of a representation of the bi-material domain geometry with neo-hookean behaviors. The boundary conditions and loadings of the experimental extension test are imposed. The materials parameters have been identified by inverse method starting from a constrained cost function minimizing the difference between the calculated displacements field and experimental displacements field obtained by digital image correlation and taking into account the reaction force as a constraint. An analysis of the model sensitivity to material parameters is presented

Experimental and numerical assessment of the mechanics of keloid-skin composites undergoing large deformations

International audienceThe aim of this paper is to evaluate the stress fields in cutaneous tissues with the long-term goal to prevent keloid development in patients. Keloids are non cancerous tumors that expand continously on the skin for a number of reasons. These specific tumors affect 11 million more patients of all age, every year and are particularly frequent in the asian and african populations. The evolution of keloids is related to genetic, biological, biophysical and biomechanical factors and are considered \complex biological systems". Keloids develop due to the excessive proliferation of fibroblasts on several specific anatomical sites on the skin, that can be identified from the value of the retractability of the tissue. Keloids grow like expansive scars on the skin surface. The relation between their development and the mechanical stress fields in and around the Keloid were studied in some papers but to our knowledge one of the few attempts at formulating a growth criterion is due to who studied the influence of the local state of stress on the keloid propagation direction. The paper presents a patient-specific methodology to identify the stress field. A keloid was observed on a patient using several imaging modalities, namely ultrasound and optical microscopy. A 3D image of the surface of the keloid was used to develop a 3D geometrical model of the keloid under consideration. The hyperelastic properties of the keloid and of the healthy skin were obtained through independent testing, on the same patient using an custom-made extensometer. A three-dimensional numerical model of the keloid-skin composite was built in order to investigate the nature of the stress field in the vicinity of the keloid-skin interface. The validity of the simulation is determined by complementary data that have been recorded during the mechanical tests.The mechanical results obtained from the numerical model are compared to displacement fields identified by Digital Image Correlation