Journal of Biomechanical Engineering-Transactions of the Asme最新文献

{"title":"Special Issue: Challenges in the Evaluation and Identification of Tissue Biomechanical Properties and Parameters.","authors":"Babak N Safa, Brianne K Connizzo","doi":"10.1115/1.4071547","DOIUrl":"10.1115/1.4071547","url":null,"abstract":"","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147576679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Inference Framework to Identify Skin Material Properties in vivo From Active Membranes.","authors":"Mark Wilkinson, Khushal Goparaju, Laura Nunez-Alvarez, Craig J Goergen, Andres F Arrieta, Adrian Buganza Tepole","doi":"10.1115/1.4071215","DOIUrl":"10.1115/1.4071215","url":null,"abstract":"<p><p>Accurate in vivo characterization of skin mechanical properties is essential for diagnostics and treatment planning across dermatological and surgical applications. Existing noninvasive techniques are limited in capturing the nonlinear and anisotropic behavior of skin. In this work, we propose a Bayesian inference framework that leverages active membranes to induce desired deformations and infer patient-specific skin properties from a measured strain field. A finite element model of skin-membrane interaction, parameterized using the Holzapfel-Gasser-Ogden model, is used to generate strain field data under various membrane actuation conditions. To overcome the computational cost of repeated simulations required for Bayesian sampling, we construct a data-driven surrogate using principal component analysis for dimensionality reduction and Gaussian process regression for rapid evaluation. Our approach enables probabilistic inference of key skin parameters, including shear modulus, fiber stiffness, dispersion, and orientation. An advantage of the proposed method is that inference of skin biomechanics does not require direct force measurements; rather, the method relies on known properties of active membranes (which can be tested ahead of time). The method does require strain field measurements. Through synthetic studies, we demonstrate that our method accurately recovers most model parameters even under moderate levels of spatially correlated noise, and that multiframe or multimembrane observations significantly enhance identifiability. These results establish the potential of active membranes as a viable platform for noninvasive, in vivo skin biomechanics assessment.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13313126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147291778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering the \"Art\" in Modeling and Simulation of the Knee Joint: Model Benchmarking.","authors":"Maryam Nazem, Thor E Andreassen, Nancy Kim, Kate Moyle, Thor F Besier, Jason P Halloran, Carl W Imhauser, Snehal Chokhandre, Marco T Y Schneider, Shady Elmasry, William Zaylor, Kevin B Shelburne, Ahmet Erdemir, Peter J Laz","doi":"10.1115/1.4070823","DOIUrl":"10.1115/1.4070823","url":null,"abstract":"<p><p>Given the strong ties to data sharing and the responsible use of resources, reproducibility of modeling and simulation practice is of paramount importance in science. Computational models in orthopedics provide insight into healthy and injured joint mechanics and can inform clinical decision-making. The KneeHub project investigated the influence of modelers' decisions and thus their \"art\" in simulation and modeling; five teams developed and calibrated knee models using the same experimental data. Model benchmarking evaluated the predictive ability of the models under loading scenarios that were not considered in the development and calibration process. The objective of this study was to evaluate the accuracy of predictions of knee-specific joint biomechanics for benchmark scenarios of simulating a resected anterior cruciate ligament (ACL) using models of one knee and a combined pivot shift loading using models of another knee. The models predicted the major trends in kinematics and kinetics; however, differences were observed in comparison to experimental data and between teams. Model-to-experiment root-mean-square (RMS) errors were up to 6.6±2.4 mm in anterior-posterior (AP) translation, 13.5±12.9 deg in internal-external (IE) rotation, and 5.3±3.4 deg in varus-valgus (VV) rotation; errors were largest in internal-external rotation, and standard deviations reflected differences between teams. While calibrated models were tuned to a similar set of conditions (albeit with different decisions), the optimized stiffness and reference length/strain of ligament structures may not fully reproduce the contributions of these structures to joint kinematics that were measured experimentally in the benchmark scenarios. As researchers often extend models beyond the conditions used to calibrate them, quantifying model accuracy and limitations with benchmarking represents a crucial step toward reproducibility and can help establish best practices for credible modeling in our community.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13154420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic Resonance Elastography With Incremental Compressive Prestrain as a Method for Mapping Ex Vivo Brain Tissue Mechanical Properties.","authors":"Olivia M Bailey, Alexa M Diano, Ali H Lateef, Kshitiz Upadhyay, Elise A Corbin, Curtis L Johnson","doi":"10.1115/1.4071136","DOIUrl":"10.1115/1.4071136","url":null,"abstract":"<p><p>Traumatic brain injury (TBI), resulting from blunt or blast impact to the head, affects approximately 1.6 million Americans per year. Brain biomechanics and injury models play a fundamental role in the TBI community as they can be used to evaluate protective equipment, refine safety standards, and improve individual treatment plans. To maximize clinical relevance, these models require accurate and precise mechanical input parameters relevant to expected injuries. Magnetic resonance elastography (MRE) offers a noninvasive method to quantify the viscoelastic properties of soft tissues by phase-encoding mechanical waves as they propagate through a tissue of interest. While MRE has advanced our understanding of brain tissue mechanics, characterizing nonlinear behavior under large deformations remains a critical challenge for improving TBI models. Here, we coupled ex vivo MRE with incremental compression to characterize brain tissue mechanics beyond the linear elastic and linear viscoelastic regimes, which are more relevant for injury. Using a custom-made MR-compatible compression device, controlled axial prestrain was applied to fresh brain tissue-agar phantoms. On average, the tissue storage modulus increased by 73.6% when approximately 6.4% of strain was applied. The resulting experimental data, namely, storage modulus and applied prestrain, were successfully fit to a phenomenological equation to output material parameters, including a nonlinearity metric. These findings provide novel, strain-dependent mechanical data for brain tissue that extend beyond small-strain assumptions. The results will improve computational models of TBI by supplying mechanical input parameters for predicting brain response under injury-relevant loading conditions.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13313124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finite Element Analysis for the Load-Bearing Femur and Tibia in the Human Knee Using an in vivo HR-pQCT Protocol.","authors":"Callie E Stirling, Steven K Boyd","doi":"10.1115/1.4071535","DOIUrl":"10.1115/1.4071535","url":null,"abstract":"<p><p>High-resolution peripheral quantitative computed tomography (HR-pQCT) combined with micro-finite element (μFE) analysis enables noninvasive assessment of bone mechanics. While widely used at the distal radius and tibia, knee modeling is challenging due to complex geometry, large model sizes, and the need for physiologically relevant boundary conditions. Standardization and reproducibility are critical for interpreting mechanical predictions. HR-pQCT images were collected from one participant with recent anterior cruciate ligament (ACL) injury for boundary condition testing and 28 healthy volunteers for reproducibility. Patient-specific μFE models incorporated anatomically shaped or rectangular simulated-polymethyl methacrylate (PMMA) support layers with variable stiffness (1500-3000 MPa) and extrusion lengths (1-7 mm). Sensitivity analyses quantified the influence of layer geometry, stiffness, and length on strain energy density (SED). Reproducibility of repeated tibial scans was assessed using root-mean-square percentage coefficient of variation (RMS %CV) and intraclass correlation coefficients (ICCs). μFE knee models contained hundreds of millions of degrees-of-freedom, with solution times of 3.6-16.4 h for anatomically shaped layers and 4.1-20.1 h for rectangular layers (32 cores). Support layer stiffness had the greatest effect on tibial SED, whereas geometry and length had minimal influence. Reproducibility across tibial scans ranged from 5.5% to 18.4% RMS %CV, with ICCs of 0.72-0.94; peri-articular trabecular regions 5-7.5 mm below the articular surface were most stable. Standardized HR-pQCT-based μFE knee modeling is essential for reproducibility. An anatomically shaped, 3-mm extruded PMMA-like support layer with ∼2500 MPa stiffness provides stable, computationally efficient load distribution and will facilitate comparability in future longitudinal knee studies.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147534474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in the Virtual Fields Method for Evaluating and Identifying Tissue Biomechanical Properties and Constitutive Parameters.","authors":"Stéphane Avril","doi":"10.1115/1.4071211","DOIUrl":"10.1115/1.4071211","url":null,"abstract":"<p><p>The derivation of material parameters for soft tissues has recently been transformed by advances in full-field measurement techniques providing high-resolution, volume-wide displacement data in soft tissues, either in vivo or in vitro during experimental tests. In this context, the virtual fields method (VFM), which is an inverse mechanics technique leveraging full-field deformation data and equilibrium principles, is flourishing. This review first provides the background on which the VFM is founded for the identification of constitutive parameters of soft tissue mechanics, focusing first on hyperelastic constitutive models. Then we review recent advances of the VFM in biomechanical contexts along the following trends: (1) formulation of a more accessible and modern VFM where virtual fields are automatically generated, (2) enhancement of robustness for the identification of regionally variable constitutive parameters, (3) extension to inelastic constitutive behavior, (4) novel in vivo applications, and (5) novel machine-learning integration. These recent advances pave the way to real-time clinical applications of the VFM for complex soft tissue modeling.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147291753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Late Triggering in Tagged Magnetic Resonance Imaging for in vivo Characterization of Brain Biomechanics During Head Rotation.","authors":"Yuan-Chiao Lu, Joy Mojumder, Ahmed Alshareef, Wen-Tung Wang, Jerry L Prince, Philip Bayly, John A Butman, Dzung L Pham","doi":"10.1115/1.4071580","DOIUrl":"10.1115/1.4071580","url":null,"abstract":"<p><p>Tagged magnetic resonance imaging (MRI) has been successfully applied for noninvasive quantification of in vivo brain deformation, enabling characterization of strain distributions under mild, noninjurious loading conditions. Previously, the double trigger (DT) method was proposed to improve temporal consistency, where an initial trigger initiates the MRI tagging pulse and a second trigger initiates image acquisition. However, this approach has two disadvantages: (1) the initial trigger was mechanical and susceptible to misalignment during connection to a fiber optic cable; (2) the time history of the tagged MRI acquisition was constrained to the interval between the initial and second triggers, thereby limiting the overall acquisition duration. In this work, a late trigger (LT) approach is proposed to eliminate mechanical triggering and the use of a fiber optic cable, reduce tag fading, and enable a longer duration time history. Gel phantom experiments demonstrate that both DT and LT approaches yield consistent strain patterns and angular kinematics. Similarly, angular motion and in-plane strain measured using in vivo two-dimensional (2D) tagged MRI show strong agreement between the DT and LT approaches; nonetheless, LT consistently provides improved tag contrast, signal-to-noise ratio (SNR), and anatomical delineation. In in vivo three-dimensional (3D) testing, LT method produces 95th percentile maximum principal strain (MPS95) strain values that closely align with those of the DT method across multiple brain regions. Collectively, the LT method demonstrates performance comparable to DT in both phantom and in vivo experiments, highlighting its potential as a physiologically robust and technically advantageous strategy for measuring brain deformation.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147619219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Pilot Study to Quantify In Vivo Meniscus Mechanics Under Load Using Magnetic Resonance Imaging.","authors":"Jamie M Benson, Maelyn Arcodia, Isabelle Larche, Darsh Patel, Dawn M Elliott, Alvin W Su","doi":"10.1115/1.4071059","DOIUrl":"10.1115/1.4071059","url":null,"abstract":"<p><p>Meniscus tears compromise its mechanical function, leading to impaired joint loading and, if left untreated, increase the risk of further joint degeneration. Surgical repair can be successful for certain tear patterns. Establishing new methods for quantifying In vivo meniscus mechanics under weightbearing facilitates improvements in surgical techniques and postoperative care. The goal of this study was to establish a method for quantifying meniscus mechanics under loading in vivo using magnetic resonance imaging (MRI). A custom MRI-compatible knee loading device was instrumented with the capability of applying loads up to 700 N at variable flexion angles. To evaluate the device, the medial and lateral menisci of healthy young participants and participants with knee pathologies were evaluated. MRI was acquired when the knees were loaded under 10% and 50% body weight at 0 deg extension and 30 deg flexion. Displacements were quantified in both menisci and articular cartilage. All participants completed the knee loading protocol without issue or discomfort. The participants with knee pathologies exhibited greater amounts of displacement compared to the healthy participants. Cartilage strain was consistent and within the expected physiological range among all participant groups. This study demonstrated the safety and effectiveness of using an in vivo MRI-compatible knee loading device that can apply axial compressive loads at varying knee flexion angles. Our results provide proof-of-concept of this technique to quantify altered joint kinematics in surgical patients with meniscus or cartilage pathologies. The novel methodology substantiates further study on patients who receive meniscus surgical procedures.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13313131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146133680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distortion Energy Drives Cellular and Mechanical Changes in Fibroblast-Seeded Collagen Scaffolds Under Cyclic Loading.","authors":"Amevi M Semodji, Dalia DeLaCruz, Anamaria G Zavala, Sean M Howard, Shaughnessy Murphy, Katherine J Fors, Zachary Pinkley, Gunes Uzer, Julia Thom Oxford, Trevor J Lujan","doi":"10.1115/1.4071132","DOIUrl":"10.1115/1.4071132","url":null,"abstract":"<p><p>Fibroblast activity in soft tissues is highly regulated by the mechanical environment of the extracellular matrix. Physical thresholds that regulate fibroblast-mediated remodeling have been previously identified using stress or strain invariants, but these thresholds depend on loading configuration, precluding the formulation of a unifying theory to predict cell response across loading environments. An alternative approach for predicting matrix remodeling is to use distortion energy, a scalar measure of deformation that accounts for both stress and strain. Here we explore whether distortion energy can predict cellular and mechanical changes across three cyclic loading configurations: uniaxial tension, uniaxial compression, and biaxial tension-compression. Collagen scaffolds were seeded with murine fibroblasts and mechano-stimulated for 7 days with a multi-axial bioreactor that cyclically applied approximately 40 J/m3 of strain energy for all loading types (error < 1%). We measured the mechanical properties of the cell-seeded constructs, before and after stimulation, and quantified cell density and fiber alignment using confocal microscopy. Multiple regression analysis revealed that changes in cell density across loading configurations had the greatest correlation with distortion energy (partial r = 0.85, p = 0.001), rather than traditional stress and strain invariants (e.g., first principal, von Mises). Distortion energy also had strong positive correlations with tensile stiffness, but not compressive properties. Overall, this study found distortion energy to be the best predictor of cellular and mechanical changes across simple and complex loads, suggesting that distortion energy may be a key physical driver for fibroblast activity and matrix remodeling.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Striking a Balance With Business and Engineering Content in Biomedical Engineering Capstone Design.","authors":"Alan W Eberhardt","doi":"10.1115/1.4070321","DOIUrl":"10.1115/1.4070321","url":null,"abstract":"<p><p>Capstone design focused on medical/assistive devices provides a golden opportunity to educate young biomedical engineers in business and entrepreneurship. What constitutes a healthy balance of engineering and business subject matter, however, remains unclear. The present paper describes the history of the senior capstone design course sequence in Biomedical Engineering (BME) at the author's institution, for which he was lead instructor. The focus is on the business content, which was first increased to encourage an entrepreneurial mind-set among students, then later decreased, based on student feedback, instructor assessment, and related grant proposal reviews, which all suggested that engineering aspects of the design process were being compromised. Business and engineering topics included in the current course sequence are described to help course designers strike a balance that provides students with the skills to be successful as design engineers with healthy business acumen.</p>","PeriodicalId":54871,"journal":{"name":"Journal of Biomechanical Engineering-Transactions of the Asme","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12755160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145472376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}