Automating Analyses of the Distal Femur Articular Geometry Based on Three-Dimensional Surface Data

Li, Kang; Tashman, Scott; Fu, Freddie H.; Harner, Christopher D.; Zhang, Xudong

doi:10.1007/s10439-010-0064-9

Cited by 25 publications

(32 citation statements)

References 28 publications

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“…The model includes a tibia plateau with a contour derived from medical images of a human subject and a semispherical femoral condyle with a radius of 20 mm. The femoral condyles can be closely approximated with ellipsoid shapes [20] and the radius of the condyle used in this study is consistent with previous studies that have measured the arc radius of the medial condyle to be in the 19–23 mm range [21–23]. …”

Section: Model Development and Methodssupporting

confidence: 83%

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Pulasani

Derakhshani

et al. 2013

Biomedical Signal Processing and Control

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Traditional finite element (FE) analysis is computationally demanding. The computational time becomes prohibitively long when multiple loading and boundary conditions need to be considered such as in musculoskeletal movement simulations involving multiple joints and muscles. Presented in this study is an innovative approach that takes advantage of the computational efficiency of both the dynamic multibody (MB) method and neural network (NN) analysis. A NN model that captures the behavior of musculoskeletal tissue subjected to known loading situations is built, trained, and validated based on both MB and FE simulation data. It is found that nonlinear, dynamic NNs yield better predictions over their linear, static counterparts. The developed NN model is then capable of predicting stress values at regions of interest within the musculoskeletal system in only a fraction of the time required by FE simulation.

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Section: Model Development and Methodssupporting

confidence: 83%

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Pulasani

Derakhshani

et al. 2013

Biomedical Signal Processing and Control

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“…2). Point A was defined as the local curvature maxima posteriorly according to Li et al 19 independently and blindly performed measurements to compare interrater reliability. After 8 weeks of the initial measurements, blinded and independent measurements were repeated to compare intrarater reliability (M.F.H.).…”

Section: Methodsmentioning

confidence: 99%

Determination of Sagittal Alignment Measurements in Distal Femurs

Hoffmann

Jones

Sietsema

et al. 2012

Journal of Orthopaedic Trauma

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The necessity for reliability and quality of intraoperative and postoperative radiographic controls of the obtained fracture reduction, implant insertion, and final healed fracture increases with popularity of less invasive indirect reduction and stabilization methods. The ability to obtain exact sagittal alignment measurements has been problematic with other studies. Two different and reliable methods of measuring sagittal plane anatomy and measurements independent of implants were confirmed using plain radiographic images.

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“…The modeling of the ellipsoid joint is initiated with the identification of ellipses that best represent actual bone shapes. This idea has been corroborated by Li et al [51] who used a similar approach for automatic analysis of the distal femur articular geometry. Thus, with the purpose of identifying the ellipses that represent actual condyle shapes, several points located on the two parasagittal planes through the condyles are considered, as Fig.…”

Section: Modeling Of the Knee Jointmentioning

confidence: 73%

Modeling of the condyle elements within a biomechanical knee model

et al. 2011

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The development of a computational multibody knee model able to capture some of the fundamental properties of the human knee articulation is presented. This desideratum is reached by including the kinetics of the real knee articulation. The research question is whether an accurate modeling of the condyle contact in the knee will lead to reproduction of the complex combination of flexion/extension, abduction/adduction, and tibial rotation observed in the real knee. The model is composed by two anatomic segments, the tibia and the femur, whose characteristics are functions of the geometric and anatomic properties of the real bones. The biomechanical model characterization is developed under the framework of multibody systems methodologies using Cartesian coordinates. The type of approach used in the proposed knee model is the joint surface contact conditions between ellipsoids, representing the two femoral condyles, and points, representing the tibial plateau and the menisci. These elements are closely fitted to the actual knee geometry. This task is undertaken by considering a parameter optimization process to replicate experimental data published in the literature, namely that by Lafortune and his coworkers in 1992. Then kinematic data in the form of flexion/extension patterns are imposed on the model corresponding to the stance phase of the human gait. From the results obtained, by performing several computational simulations, it can be observed that the knee model approximates the average secondary motion patterns observed in the literature. Because the literature reports considerable inter-individual differences in the secondary motion patterns, the knee model presented here is also used to check whether it is possible to reproduce the observed differences with reasonable variations of bone shape parameters. This task is accomplished by a parameter study, in which the main variables that define the geometry of condyles are taken into account. It was observed that the data reveal a difference in secondary kinematics of the knee in flexion versus extension. The likely explanation for this fact is the elastic component of the secondary motions created by the combination of joint forces and soft tissue deformations. The proposed knee model is, therefore, used to investigate whether this observed behavior can be explained by reasonable elastic deformations of the points representing the menisci in the model.

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Automating Analyses of the Distal Femur Articular Geometry Based on Three-Dimensional Surface Data

Cited by 25 publications

References 28 publications

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Application of neural networks for the prediction of cartilage stress in a musculoskeletal system

Determination of Sagittal Alignment Measurements in Distal Femurs

Modeling of the condyle elements within a biomechanical knee model

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