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Fracture Risk of the Proximal Femur in Osteoporosis: A Closer Look at the Role of Geometry
Full text linkL'abstract è presente nell'allegato / the abstract is in the attachmen
Dataset related to VVUQ main activities to assess the credibility of the Bologna Biomechanical Computed Tomography in silico methodology
No full textThis dataset contains the details (inputs and outputs) of the main VVUQ activities performed in order to assess the credibility of Bologna Biomechanical Computed Tomography (BBCT)-hip according to ASMEV&V40-2018. BBCT-hip model calculates ARF0, the hip fracture risk upon falling, by modelling a fall to the side. In principle, ARF0 is identified by calculating possible impact forces derived from a fall (through a stochastic mathematical model) and by assessing which of those, exceeding the load to failure (determined through a patient-specific finite element model), lead to a fracture event. More in detail, BBCT-hip uses a stochastic mathematical model to simulate 1,000,000 falls of a body of the height and weight equal to those of the patient, each with initial conditions assigned randomly according to specific probability distributions, and for each of these falls predicts the resulting impact force. In parallel, a patient-specific Finite Element (FE) model of the femur informed by the patient’s QCT data is run 28 times, varying the femur orientation at the impact (femoral impact pose). For each impact pose, the load to failure, i.e. the intensity of the force required to fracture the femur, is computed based on principal strains. The FE model-derived loads to failure inform a reduced-order model (response surface) which allows inferring the magnitude of the load to failure for each possible impact direction at a reasonable computational cost. The surrogate biomarker ARF0 is calculated as the ratio of the number of simulated falls that the model predicts would cause a fracture divided by the total number of simulated falls
A statistical shape analysis for the assessment of the main geometrical features of the distal femoral medullary canal
Full text linkStatistical Shape Models (SSMs) are widely used in orthopedics to extract the main shape features from bone regions (e.g., femur). This study aims to develop an SSM of the femoral medullary canal, investigate its anatomical variability, and assess variations depending on canal length. The canals were isolated from 72 CT femur scans, through a threshold-based segmentation. A region of interest (ROI) was selected; sixteen segments were extracted from the ROI, ranging from 25% of the full length down to the most distal segment. An SSM was developed to identify the main modes of variation for each segment. The number of Principal Components (PCs) needed to explain at least 90% of the shape variance were three/four based on the length of the canal segment. The study examined the relationship between the identified PCs and geometric parameters like length, radius of curvature, ellipticity, mean diameter, and conicity, reporting range and percentage variation of these parameters for each segment. The SSMs provide insights into the anatomical variability of the femoral canal, emphasizing the importance of considering different segments to capture shape variations at various canal length. These findings can contribute for the design of personalized orthopedic implants involving the distal femur
HFValid collection: Hip-Fracture validation collection
Full text linkThe HFValid dataset is composed of 101 calibrated CT scans of whole femurs, the corresponding segmentations, and selected anatomical landmarks. The CT scans were collected at Rizzoli Orthopaedic Institute (IOR) from 1999 to 2016 for surgical planning of hip arthroplasty at the contralateral femur.
In this version the image calibrations have been updated based a shrinked segmentation of the phantom so as to prevent partial volume effects, and the femur reference coordinates of Pat073 have been fixed
HFValid collection: Hip-Fracture validation collection
Full text linkThe HFValid dataset is composed of 101 calibrated CT scans of whole femurs, the corresponding segmentations, and selected anatomical landmarks. The CT scans were collected at Rizzoli
Orthopaedic Institute (IOR) from 1999 to 2016 for surgical planning of hip arthroplasty at the contralateral femur
Are mechanical parameters of surgical meshes related to structure properties? A multiple regression analysis
No full textDevelopment of an animal-free methodology for mechanical performance assessment of engineered skin substitutes
Full text linkOrthopedic biomechanics: multibody analysis
No full textJoints of the musculoskeletal apparatus are characterized by high complexity, being described by three-dimensional kinematics, which results from the concurrence of translational and rotational motions among the articulating bones forming the joint. The complexity of the kinematics derives from the interaction of different anatomical structures, and, in particular, ligaments play a critical role in defining joints’ range of motion, since they represent the major articular restraining elements. An alteration of the balancing among the constraining actions exerted by the ligaments, together with the other soft tissue surrounding the joint, leads to modifications in the joint kinematics and, consequently, to the establishment of detrimental intraarticular loading conditions. In order to alleviate pain caused by pathological conditions and to restore the functionality of the joint, different orthopedic surgical approaches can be pursued, such as corrective osteotomies and prosthesis implantation. Over the past few years, the introduction of even more sophisticated mechanical tools and computer-assisted surgical systems has made it possible to improve the accuracy of the procedures and, thus, to enhance surgical outcomes. Nevertheless, obtaining essential information such as intraarticular contact pressure and soft tissue tensioning still remains a challenge or at times even impossible to measure during a surgical intervention in an operative room. In this context, the integration of computational simulations into the surgical decision-making process appears to be an appealing solution to address this problem and to quantify surgical parameters that, usually, are just qualitatively evaluated. In particular, the multibody modeling of joints has proven to be an effective approach to estimating significant quantities, such as forces related to ligaments, tendons, muscles, and intraarticular contacts, in addition to relative motions between bones. Moreover, the development of even more accurate patient-specific biomechanical models, coupled with surgical navigation systems and specifically designed sensing devices, could lead to a better understanding of the joint biomechanics as well as to the implementation of valuable tools aimed at supporting surgeons throughout the pre-, intra-, and postoperative phases, providing additional data enabling foreseeing consequences of surgical interventions and also improving actual surgical techniques. In this chapter, various multibody modeling strategies are proposed, which might prove useful in investigating specific orthopedic issues related to the biomechanical behavior of anatomical and artificial joints. In addition, case studies related to anatomical, injured, and prosthetic joints are presented, in order to provide application examples of the modeling strategies proposed
Comparing the predictions of CT-based subject-specific finite element models of human metastatic vertebrae with digital volume correlation measurements
Full text linkSeveral conditions can increase the incidence of vertebral fragility fractures, including metastatic bone disease. Computational tools could help clinicians estimate the risk of vertebral fracture in these patients; however, comparison with in vitro data is mandatory before using them in clinical practice. Nine spine segments were tested under compression and imaged with micro-computed tomography (µCT). The displacement field was calculated for each vertebra using a global digital volume correlation (DVC) approach. Subject-specific homogenised finite element models of each vertebra were built from µCT images, applying experimentally matched boundary conditions at the endplates. Numerical and experimental displacements, reaction forces, and locations showing higher strain concentrations were eventually compared. Additionally, given that µCT cannot be performed in clinical settings, the outcomes of a µCT-based model were also compared to those of a model built from clinical CT scans of the same specimen. Good agreement between DVC and µCT-based FE displacements was found, both for healthy (R
2 = 0.69 ÷ 0.83, RMSE = 3 ÷ 22%, max error < 45 μm) and metastatic (R
2 = 0.64 ÷ 0.93, RMSE = 5 ÷ 18%, max error < 54 μm) vertebrae. Strong correlations were found between µCT-based and clinical CT-based FE model outcomes (R
2 = 0.99, RMSE < 1.3%, max difference = 6 μm). Furthermore, the models qualitatively identified the most deformed regions identified with the experiments. In conclusion, the combination of experimental full-field technique and in-silico modelling enabled the development of a promising pipeline to validate bone strength predictors in the elastic range. Further improvements are needed to analyse vertebral post-yield behaviour better
Assessing the Impact of Morphological Parameters on the Mechanical Behavior of Synthetic Meshes. A Multivariate Regression Approach
No full textThe impact of morphological and mechanical parameters of surgical meshes on the healing processes and patient comfort after abdominal repair surgery is widely accepted. However, how the structure of the knitted pattern of synthetic meshes affects the mechanical behavior remains primarily theoretical. The objective of this study was therefore to assess the correlation between these key factors, identifying the most crucial morphological parameters able to support the design of new meshes. In this perspective, morphological parameters related to pore size, shape, and orientation were computed based on high-resolution images using the poreScanner app and the Matlab Image Processing toolbox. Additional parameters such as weight and thickness were measured through high-precision instruments. Concurrently, 12 mechanical parameters were assessed by executing a comprehensive testing protocol. Multivariate regression models were implemented, each using one to five morphological parameters as independent variables and one of the 12 mechanical parameters as dependent variables. A leave-one-out (LOO) validation algorithm was then employed to estimate the models' performance, robustness, and accuracy for potential future predictions. Regression models showed high coefficients of determination (R2 ≥ 0.8), except for uniaxial strains (0.59 0.65) for 5 out of 12 mechanical parameters, whereas moderate predictive capabilities (R2 > 0.55) for one model. Promising results demonstrate a quantifiable relationship between pore characteristics and mechanical behavior. Thanks to further validation using different meshes, the models could be beneficial for all stakeholders involved in this field, from patients to manufacturers
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