The Experts below are selected from a list of 74940 Experts worldwide ranked by ideXlab platform
James D Johnston - One of the best experts on this subject based on the ideXlab platform.
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mechanical metrics of the proximal tibia are precise and differentiate osteoarthritic and normal knees a finite element study
Scientific Reports, 2018Co-Authors: Hanieh Arjmand, Saija A Kontulainen, David R Wilson, Majid Nazemi, Christine E Mclennan, David J Hunter, James D JohnstonAbstract:Our objective was to identify precise mechanical metrics of the proximal tibia which differentiated OA and normal knees. We developed subject-specific FE models for 14 participants (7 OA, 7 normal) who were imaged three times each for assessing precision (repeatability). We assessed various mechanical metrics (minimum principal and von Mises stress and strain as well as Structural Stiffness) across the proximal tibia for each subject. In vivo precision of these mechanical metrics was assessed using CV%RMS. We performed parametric and non-parametric statistical analyses and determined Cohen's d effect sizes to explore differences between OA and normal knees. For all FE-based mechanical metrics, average CV%RMS was less than 6%. Minimum principal stress was, on average, 75% higher in OA versus normal knees while minimum principal strain values did not differ. No difference was observed in Structural Stiffness. FE modeling could precisely quantify and differentiate mechanical metrics variations in normal and OA knees, in vivo. This study suggests that bone stress patterns may be important for understanding OA pathogenesis at the knee.
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optimizing finite element predictions of local subchondral bone Structural Stiffness using neural network derived density modulus relationships for proximal tibial subchondral cortical and trabecular bone
Clinical Biomechanics, 2017Co-Authors: Majid S Nazemi, Morteza Amini, Saija A Kontulainen, Jaques S Milner, David W Holdsworth, Bassam A Masri, David R Wilson, James D JohnstonAbstract:Abstract Background Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local Structural Stiffness at the proximal tibial subchondral surface. Methods Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured Stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting Stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting Stiffness. Findings Finite element modeling predicted 81% of experimental Stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5 g/cm3 density. Interpretation In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral Structural Stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions.
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prediction of local proximal tibial subchondral bone Structural Stiffness using subject specific finite element modeling effect of selected density modulus relationship
Clinical Biomechanics, 2015Co-Authors: Majid S Nazemi, Morteza Amini, Saija A Kontulainen, Jaques S Milner, David W Holdsworth, Bassam A Masri, David R Wilson, James D JohnstonAbstract:Abstract Background Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain initiation. Calculation of bone elastic moduli from image data is a basic step when constructing finite element models. However, different relationships between elastic moduli and imaged density (known as density–modulus relationships) have been reported in the literature. The objective of this study was to apply seven different trabecular-specific and two cortical-specific density–modulus relationships from the literature to finite element models of proximal tibia subchondral bone, and identify the relationship(s) that best predicted experimentally measured local subchondral Structural Stiffness with highest explained variance and least error. Methods Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using published density–modulus relationships and mapped to corresponding finite element models. Proximal tibial Structural Stiffness values were compared to experimentally measured Stiffness values from in-situ macro-indentation testing directly on the subchondral bone surface (47 indentation points). Findings Regression lines between experimentally measured and finite element calculated Stiffness had R 2 values ranging from 0.56 to 0.77. Normalized root mean squared error varied from 16.6% to 337.6%. Interpretation Of the 21 evaluated density–modulus relationships in this study, Goulet combined with Snyder and Schneider or Rho appeared most appropriate for finite element modeling of local subchondral bone Structural Stiffness. Though, further studies are needed to optimize density–modulus relationships and improve finite element estimates of local subchondral bone Structural Stiffness.
R S Lakes - One of the best experts on this subject based on the ideXlab platform.
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enhancement in piezoelectric sensitivity via negative Structural Stiffness
Journal of Intelligent Material Systems and Structures, 2016Co-Authors: H Kalathur, R S LakesAbstract:Effective piezoelectric sensitivity of bimorph strip actuators was enhanced by negative Structural Stiffness. Negative Stiffness was achieved in brass strips post-buckled in compression to an “S” s...
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column dampers with negative Stiffness high damping at small amplitude
Smart Materials and Structures, 2013Co-Authors: H Kalathur, R S LakesAbstract:High Structural damping combined with high initial Stiffness is achieved at small amplitude via negative Stiffness elements. These elements consist of columns in the vicinity of the post-buckling transition between contact of flat surfaces and edges of the ends for which negative incremental Structural Stiffness occurs. The column configuration provides a high initial Structural Stiffness equal to the intrinsic Stiffness of the column material. Columns of the polymers polymethyl methacrylate (PMMA) and polycarbonate were used. By tuning the pre-strain, a very high mechanical damping was achieved for small amplitude oscillations. The product of effective Stiffness and effective damping as a figure of merit |Eeff|tanδeff of about 1.5 GPa was achieved for polymer column dampers in the linear domain and about 1.62 GPa in the small amplitude nonlinear domain. For most materials this value generally never exceeds 0.6 GPa.
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advanced damper with high Stiffness and high hysteresis damping based on negative Structural Stiffness
International Journal of Solids and Structures, 2013Co-Authors: Liang Dong, R S LakesAbstract:Abstract High Structural damping combined with high Stiffness is achieved by negative Stiffness elements. Negative incremental Structural Stiffness occurs when a column with flat ends is subjected to snap-through buckling. Large hysteresis (i.e., high damping) can be achieved provided the ends of the column undergo tilting from flat to edge contact. The column configuration provides high Structural Stiffness. Stable axial dampers with initial modulus similar to that of the parent material and with enhanced damping were designed built and tested. Effective damping of approximately two and Stiffness-damping product of approximately 200 GPa were achieved in such dampers consisting of stainless steel columns. This is a significant improvement for this figure of merit (i.e., the Stiffness-damping product), which generally cannot exceed 0.6 GPa for currently used damping systems.
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advanced damper with negative Structural Stiffness elements
Smart Materials and Structures, 2012Co-Authors: Liang Dong, R S LakesAbstract:Negative Stiffness is understood as the occurrence of a force in the same direction as the imposed deformation. Structures and composites with negative Stiffness elements enable a large amplification in damping. It is shown in this work, using an experimental approach, that when a flexible flat-ends column is aligned in a post-buckled condition, a negative Structural Stiffness and large hysteresis (i.e., high damping) can be achieved provided the ends of the column undergo tilting from flat to edge contact. Stable axial dampers with initial modulus equivalent to that of the parent material and with enhanced damping were designed and built using constrained negative Stiffness effects entailed by post-buckled press-fit flat-ends columns. Effective damping of approximately 1 and an effective Stiffness–damping product of approximately 1.3 GPa were achieved in such stable axial dampers consisting of PMMA columns. This is a considerable improvement for this figure of merit (i.e., the Stiffness–damping product), which generally cannot exceed 0.6 GPa for currently used damping layers.
Majid S Nazemi - One of the best experts on this subject based on the ideXlab platform.
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optimizing finite element predictions of local subchondral bone Structural Stiffness using neural network derived density modulus relationships for proximal tibial subchondral cortical and trabecular bone
Clinical Biomechanics, 2017Co-Authors: Majid S Nazemi, Morteza Amini, Saija A Kontulainen, Jaques S Milner, David W Holdsworth, Bassam A Masri, David R Wilson, James D JohnstonAbstract:Abstract Background Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local Structural Stiffness at the proximal tibial subchondral surface. Methods Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured Stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting Stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting Stiffness. Findings Finite element modeling predicted 81% of experimental Stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5 g/cm3 density. Interpretation In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral Structural Stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions.
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prediction of local proximal tibial subchondral bone Structural Stiffness using subject specific finite element modeling effect of selected density modulus relationship
Clinical Biomechanics, 2015Co-Authors: Majid S Nazemi, Morteza Amini, Saija A Kontulainen, Jaques S Milner, David W Holdsworth, Bassam A Masri, David R Wilson, James D JohnstonAbstract:Abstract Background Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain initiation. Calculation of bone elastic moduli from image data is a basic step when constructing finite element models. However, different relationships between elastic moduli and imaged density (known as density–modulus relationships) have been reported in the literature. The objective of this study was to apply seven different trabecular-specific and two cortical-specific density–modulus relationships from the literature to finite element models of proximal tibia subchondral bone, and identify the relationship(s) that best predicted experimentally measured local subchondral Structural Stiffness with highest explained variance and least error. Methods Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using published density–modulus relationships and mapped to corresponding finite element models. Proximal tibial Structural Stiffness values were compared to experimentally measured Stiffness values from in-situ macro-indentation testing directly on the subchondral bone surface (47 indentation points). Findings Regression lines between experimentally measured and finite element calculated Stiffness had R 2 values ranging from 0.56 to 0.77. Normalized root mean squared error varied from 16.6% to 337.6%. Interpretation Of the 21 evaluated density–modulus relationships in this study, Goulet combined with Snyder and Schneider or Rho appeared most appropriate for finite element modeling of local subchondral bone Structural Stiffness. Though, further studies are needed to optimize density–modulus relationships and improve finite element estimates of local subchondral bone Structural Stiffness.
David R Wilson - One of the best experts on this subject based on the ideXlab platform.
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mechanical metrics of the proximal tibia are precise and differentiate osteoarthritic and normal knees a finite element study
Scientific Reports, 2018Co-Authors: Hanieh Arjmand, Saija A Kontulainen, David R Wilson, Majid Nazemi, Christine E Mclennan, David J Hunter, James D JohnstonAbstract:Our objective was to identify precise mechanical metrics of the proximal tibia which differentiated OA and normal knees. We developed subject-specific FE models for 14 participants (7 OA, 7 normal) who were imaged three times each for assessing precision (repeatability). We assessed various mechanical metrics (minimum principal and von Mises stress and strain as well as Structural Stiffness) across the proximal tibia for each subject. In vivo precision of these mechanical metrics was assessed using CV%RMS. We performed parametric and non-parametric statistical analyses and determined Cohen's d effect sizes to explore differences between OA and normal knees. For all FE-based mechanical metrics, average CV%RMS was less than 6%. Minimum principal stress was, on average, 75% higher in OA versus normal knees while minimum principal strain values did not differ. No difference was observed in Structural Stiffness. FE modeling could precisely quantify and differentiate mechanical metrics variations in normal and OA knees, in vivo. This study suggests that bone stress patterns may be important for understanding OA pathogenesis at the knee.
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optimizing finite element predictions of local subchondral bone Structural Stiffness using neural network derived density modulus relationships for proximal tibial subchondral cortical and trabecular bone
Clinical Biomechanics, 2017Co-Authors: Majid S Nazemi, Morteza Amini, Saija A Kontulainen, Jaques S Milner, David W Holdsworth, Bassam A Masri, David R Wilson, James D JohnstonAbstract:Abstract Background Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local Structural Stiffness at the proximal tibial subchondral surface. Methods Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured Stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting Stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting Stiffness. Findings Finite element modeling predicted 81% of experimental Stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5 g/cm3 density. Interpretation In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral Structural Stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions.
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prediction of local proximal tibial subchondral bone Structural Stiffness using subject specific finite element modeling effect of selected density modulus relationship
Clinical Biomechanics, 2015Co-Authors: Majid S Nazemi, Morteza Amini, Saija A Kontulainen, Jaques S Milner, David W Holdsworth, Bassam A Masri, David R Wilson, James D JohnstonAbstract:Abstract Background Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain initiation. Calculation of bone elastic moduli from image data is a basic step when constructing finite element models. However, different relationships between elastic moduli and imaged density (known as density–modulus relationships) have been reported in the literature. The objective of this study was to apply seven different trabecular-specific and two cortical-specific density–modulus relationships from the literature to finite element models of proximal tibia subchondral bone, and identify the relationship(s) that best predicted experimentally measured local subchondral Structural Stiffness with highest explained variance and least error. Methods Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using published density–modulus relationships and mapped to corresponding finite element models. Proximal tibial Structural Stiffness values were compared to experimentally measured Stiffness values from in-situ macro-indentation testing directly on the subchondral bone surface (47 indentation points). Findings Regression lines between experimentally measured and finite element calculated Stiffness had R 2 values ranging from 0.56 to 0.77. Normalized root mean squared error varied from 16.6% to 337.6%. Interpretation Of the 21 evaluated density–modulus relationships in this study, Goulet combined with Snyder and Schneider or Rho appeared most appropriate for finite element modeling of local subchondral bone Structural Stiffness. Though, further studies are needed to optimize density–modulus relationships and improve finite element estimates of local subchondral bone Structural Stiffness.
Saija A Kontulainen - One of the best experts on this subject based on the ideXlab platform.
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mechanical metrics of the proximal tibia are precise and differentiate osteoarthritic and normal knees a finite element study
Scientific Reports, 2018Co-Authors: Hanieh Arjmand, Saija A Kontulainen, David R Wilson, Majid Nazemi, Christine E Mclennan, David J Hunter, James D JohnstonAbstract:Our objective was to identify precise mechanical metrics of the proximal tibia which differentiated OA and normal knees. We developed subject-specific FE models for 14 participants (7 OA, 7 normal) who were imaged three times each for assessing precision (repeatability). We assessed various mechanical metrics (minimum principal and von Mises stress and strain as well as Structural Stiffness) across the proximal tibia for each subject. In vivo precision of these mechanical metrics was assessed using CV%RMS. We performed parametric and non-parametric statistical analyses and determined Cohen's d effect sizes to explore differences between OA and normal knees. For all FE-based mechanical metrics, average CV%RMS was less than 6%. Minimum principal stress was, on average, 75% higher in OA versus normal knees while minimum principal strain values did not differ. No difference was observed in Structural Stiffness. FE modeling could precisely quantify and differentiate mechanical metrics variations in normal and OA knees, in vivo. This study suggests that bone stress patterns may be important for understanding OA pathogenesis at the knee.
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optimizing finite element predictions of local subchondral bone Structural Stiffness using neural network derived density modulus relationships for proximal tibial subchondral cortical and trabecular bone
Clinical Biomechanics, 2017Co-Authors: Majid S Nazemi, Morteza Amini, Saija A Kontulainen, Jaques S Milner, David W Holdsworth, Bassam A Masri, David R Wilson, James D JohnstonAbstract:Abstract Background Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local Structural Stiffness at the proximal tibial subchondral surface. Methods Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured Stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting Stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting Stiffness. Findings Finite element modeling predicted 81% of experimental Stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5 g/cm3 density. Interpretation In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral Structural Stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions.
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prediction of local proximal tibial subchondral bone Structural Stiffness using subject specific finite element modeling effect of selected density modulus relationship
Clinical Biomechanics, 2015Co-Authors: Majid S Nazemi, Morteza Amini, Saija A Kontulainen, Jaques S Milner, David W Holdsworth, Bassam A Masri, David R Wilson, James D JohnstonAbstract:Abstract Background Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain initiation. Calculation of bone elastic moduli from image data is a basic step when constructing finite element models. However, different relationships between elastic moduli and imaged density (known as density–modulus relationships) have been reported in the literature. The objective of this study was to apply seven different trabecular-specific and two cortical-specific density–modulus relationships from the literature to finite element models of proximal tibia subchondral bone, and identify the relationship(s) that best predicted experimentally measured local subchondral Structural Stiffness with highest explained variance and least error. Methods Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using published density–modulus relationships and mapped to corresponding finite element models. Proximal tibial Structural Stiffness values were compared to experimentally measured Stiffness values from in-situ macro-indentation testing directly on the subchondral bone surface (47 indentation points). Findings Regression lines between experimentally measured and finite element calculated Stiffness had R 2 values ranging from 0.56 to 0.77. Normalized root mean squared error varied from 16.6% to 337.6%. Interpretation Of the 21 evaluated density–modulus relationships in this study, Goulet combined with Snyder and Schneider or Rho appeared most appropriate for finite element modeling of local subchondral bone Structural Stiffness. Though, further studies are needed to optimize density–modulus relationships and improve finite element estimates of local subchondral bone Structural Stiffness.
