Browsing by Author "Babazadeh-Naseri, Ata"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    A computational method for estimating trunk muscle activations during gait using lower extremity muscle synergies
    (Frontiers Media S.A., 2022) Li, Geng; Ao, Di; Vega, Marleny M.; Shourijeh, Mohammad S.; Zandiyeh, Payam; Chang, Shuo-Hsiu; Lewis, Valerae O.; Dunbar, Nicholas J.; Babazadeh-Naseri, Ata; Baines, Andrew J.; Fregly, Benjamin J.; Rice Computational Neuromechanics Laboratory
    One of the surgical treatments for pelvic sarcoma is the restoration of hip function with a custom pelvic prosthesis after cancerous tumor removal. The orthopedic oncologist and orthopedic implant company must make numerous often subjective decisions regarding the design of the pelvic surgery and custom pelvic prosthesis. Using personalized musculoskeletal computer models to predict post-surgery walking function and custom pelvic prosthesis loading is an emerging method for making surgical and custom prosthesis design decisions in a more objective manner. Such predictions would necessitate the estimation of forces generated by muscles spanning the lower trunk and all joints of the lower extremities. However, estimating trunk and leg muscle forces simultaneously during walking based on electromyography (EMG) data remains challenging due to the limited number of EMG channels typically used for measurement of leg muscle activity. This study developed a computational method for estimating unmeasured trunk muscle activations during walking using lower extremity muscle synergies. To facilitate the calibration of an EMG-driven model and the estimation of leg muscle activations, EMG data were collected from each leg. Using non-negative matrix factorization, muscle synergies were extracted from activations of leg muscles. On the basis of previous studies, it was hypothesized that the time-varying synergy activations were shared between the trunk and leg muscles. The synergy weights required to reconstruct the trunk muscle activations were determined through optimization. The accuracy of the synergy-based method was dependent on the number of synergies and optimization formulation. With seven synergies and an increased level of activation minimization, the estimated activations of the erector spinae were strongly correlated with their measured activity. This study created a custom full-body model by combining two existing musculoskeletal models. The model was further modified and heavily personalized to represent various aspects of the pelvic sarcoma patient, all of which contributed to the estimation of trunk muscle activations. This proposed method can facilitate the prediction of post-surgery walking function and pelvic prosthesis loading, as well as provide objective evaluations for surgical and prosthesis design decisions.
  • Thumbnail Image
    Item
    Computational Framework for Designing Patient-specific, 3D-printable, and Stress-shielding Resistant Pelvic Implants
    (2022-01-11) Babazadeh-Naseri, Ata; Akin, John Ed; Higgs III, C Fred
    Pelvic resections to remove bone tumors can lead to large bone defects, which vary based on the location and extent of the tumor. Recent advancements in 3D-printing of biocompatible alloys, along with high-resolution computed tomography (CT) imaging, have made custom implants a viable option for reconstructing and restoring load-bearing ability and walking function in individuals with pelvic sarcoma. However, prosthetic re-construction of the pelvis has been associated with high complication rates and poor long-term functional outcomes. Aseptic loosening is one of the leading causes of post-surgery complications. A significant contributor to implant loosening is bone resorption due to stress-shielding. This phenomenon occurs in peri-prosthetic bone when the stiffer implant shields the less stiff bone from the mechanical stimuli required for maintaining healthy bone density. Incorporating porosities in implants using 3D-printable lattices can reduce their stiffness, hence abating the stress-shielding. Moreover, implant fixation to bone can be improved by promoting bony ingrowth within the implant. This thesis presents a computational framework for designing custom pelvic im-plants with enhanced osseointegration and stress-shielding resistance. Computational models, capable of predicting bone stresses after surgery, were needed for evaluating stress-shielding. First, patient-specific finite element models of bone with heterogeneous properties were developed. Different bone material mapping methods were investigated. The most suitable methods in terms of model convergence were identified. The long-term fixation of the implant to bone through osseointegration was also addressed. Second, a computational approach was outlined for evaluating the performance of implant lattices for bone ingrowth and matching bone properties. Superior biocompatibility of the im-plant with the peri-prosthetic bone was achieved by tuning the anisotropic elastic proper-ties of the implant to match those of bone. We used a previously validated model for pre-dicting bone growth inside porous implants. Thus, lattice designs with balanced osseoin-tegration and bone material properties matching performance were realized. Finally, a topology optimization formulation was developed for designing stress-shielding resistant pelvic implants. This formulation yielded a design that significantly reduced the predicted volume of bone resorption at the bone-implant interface compared to the solid implant. Therefore, a pelvic implant design with the potential for improved long-term stability was obtained.
  • Thumbnail Image
    Item
    Evaluation of finite element modeling methods for predicting compression screw failure in a custom pelvic implant
    (Frontiers Media S.A., 2024) Zhu, Yuhui; Babazadeh-Naseri, Ata; Brake, Matthew R. W.; Akin, John E.; Li, Geng; Lewis, Valerae O.; Fregly, Benjamin J.
    Introduction: Three-dimensional (3D)-printed custom pelvic implants have become a clinically viable option for patients undergoing pelvic cancer surgery with resection of the hip joint. However, increased clinical utilization has also necessitated improved implant durability, especially with regard to the compression screws used to secure the implant to remaining pelvic bone. This study evaluated six different finite element (FE) screw modeling methods for predicting compression screw pullout and fatigue failure in a custom pelvic implant secured to bone using nine compression screws. Methods: Three modeling methods (tied constraints (TIE), bolt load with constant force (BL-CF), and bolt load with constant length (BL-CL)) generated screw axial forces using functionality built into Abaqus FE software; while the remaining three modeling methods (isotropic pseudo-thermal field (ISO), orthotropic pseudo-thermal field (ORT), and equal-and-opposite force field (FOR)) generated screw axial forces using iterative physics-based relationships that can be implemented in any FE software. The ability of all six modeling methods to match specified screw pretension forces and predict screw pullout and fatigue failure was evaluated using an FE model of a custom pelvic implant with total hip replacement. The applied hip contact forces in the FE model were estimated at two locations in a gait cycle. For each of the nine screws in the custom implant FE model, likelihood of screw pullout failure was predicted using maximum screw axial force, while likelihood of screw fatigue failure was predicted using maximum von Mises stress. Results: The three iterative physics-based modeling methods and the non-iterative Abaqus BL-CL method produced nearly identical predictions for likelihood of screw pullout and fatigue failure, while the other two built-in Abaqus modeling methods yielded vastly different predictions. However, the Abaqus BL-CL method required the least computation time, largely because an iterative process was not needed to induce specified screw pretension forces. Of the three iterative methods, FOR required the fewest iterations and thus the least computation time. Discussion: These findings suggest that the BL-CL screw modeling method is the best option when Abaqus is used for predicting screw pullout and fatigue failure in custom pelvis prostheses, while the iterative physics-based FOR method is the best option if FE software other than Abaqus is used.
  • Thumbnail Image
    Item
    Finite element analysis of screw fixation durability under multiple boundary and loading conditions for a custom pelvic implant
    (Elsevier, 2023) Zhu, Yuhui; Babazadeh-Naseri, Ata; Dunbar, Nicholas J.; Brake, Matthew R.W.; Zandiyeh, Payam; Li, Geng; Leardini, Alberto; Spazzoli, Benedetta; Fregly, Benjamin J.
    Despite showing promising functional outcomes for pelvic reconstruction after sarcoma resection, custom-made pelvic implants continue to exhibit high complication rates due to fixation failures. Patient-specific finite element models have been utilized by researchers to evaluate implant durability. However, the effect of assumed boundary and loading conditions on failure analysis results of fixation screws remains unknown. In this study, the postoperative stress distributions in the fixation screws of a state-of-the-art custom-made pelvic implant were simulated, and the risk of failure was estimated under various combinations of two bone-implant interaction models (tied vs. frictional contact) and four load cases from level-ground walking and stair activities. The study found that the average weighted peak von Mises stress could increase by 22-fold when the bone-implant interactions were modeled with a frictional contact model instead of a tied model, and the likelihood of fatigue and pullout failure for each screw could change dramatically when different combinations of boundary and loading conditions were used. The inclusion of additional boundary and loading conditions led to a more reliable analysis of fixation durability. These findings demonstrated the importance of simulating multiple boundary conditions and load cases for comprehensive implant design evaluation using finite element analysis.