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18 projects in result set.
Open Knee(s): Virtual Biomechanical Representations of the Knee Joint
- Open Knee(s) was aimed to provide free access to three-dimensional finite element representations of the knee joint (<A HREF="https://doi.org/10.1007/s10439-022-03074-0">https://doi.org/10.1007/s10439-022-03074-0</A>). The development platform remains open to enable any interested party to use, test, and edit the model; in a nut shell get involved with the project.
This study was primarily funded by the National Institute of General Medical Sciences, National Institutes of Health (R01GM104139) and in part by National Institute of Biomedical Imaging and Bioengineering (R01EB024573 and R01EB025212). Previous activities leading towards this project had been partially funded by the National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (R01EB009643).
Open Knee(s) by Open Knee(s) Development Team is licensed under a <A HREF="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</A>.
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Registered: 2010-02-18 20:41 |
OpenArm: Volumetric & Time Series Models of Muscle Deformation
- We invite anyone in the research community to use the OpenArm and OpenArm Multisensor data sets to validate existing muscle deformation models or to devise new ones.
Full details can be found in the following papers:
Laura A. Hallock, Bhavna Sud, Chris Mitchell, Eric Hu, Fayyaz Ahamed, Akash Velu, Amanda Schwartz, and Ruzena Bajcsy. "Toward Real-Time Muscle Force Inference and Device Control via Optical-Flow-Tracked Muscle Deformation." In IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE). IEEE, 2021. (Under review.)
Laura Hallock, Akash Velu, Amanda Schwartz, and Ruzena Bajcsy. "Muscle deformation correlates with output force during isometric contraction." In IEEE RAS/EMBS International Conference on Biomedical Robotics & Biomechatronics (BioRob). IEEE, 2020. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/hallock2020biorob.)
Yonatan Nozik*, Laura A. Hallock*, Daniel Ho, Sai Mandava, Chris Mitchell, Thomas Hui Li, and Ruzena Bajcsy, "OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics," in International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2019. *Equal contribution. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/nozikhallock2019embc.)
Laura Hallock, Akira Kato, and Ruzena Bajcsy. "Empirical quantification and modeling of muscle deformation: Toward ultrasound-driven assistive device control." In IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018. (Available at https://people.eecs.berkeley.edu/~lhallock/publication/hallock2018icra.)
This project is currently in development in the Human-Assistive Robotic Technologies (HART) Lab at the University of California, Berkeley (http://hart.berkeley.edu). | |
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Registered: 2018-11-28 20:40 |
Grand Challenge Competition to Predict In Vivo Knee Loads
- Knowledge of muscle and joint contact forces during gait is necessary to characterize muscle coordination and function as well as joint and soft-tissue loading. Musculoskeletal modeling and simulation is required to estimate muscle and joint contact forces, since direct measurement is not feasible under normal conditions. This project provides the biomechanics community with a unique and comprehensive data set to validate muscle and contact force estimates in the knee. This data set includes motion capture, ground reaction, EMG, tibial contact force, and strength data collected from a subject implanted with an instrumented knee prosthesis. | |
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Activity Percentile: 94.66 Registered: 2009-07-14 23:24 |
Predicting Cell Deformation from Body Level Mechanical Loads
- This project is a NIBIB/NIH funded study (1R01EB009643-01) to establish models and computational platforms to predict cellular deformations from joint level mechanical loading.
Collaborators:
Ahmet Erdemir (PI), Amit Vasanji, Jason Halloran (Cleveland Clinic)
Cees Oomens, Frank Baaijens (Eindhoven University of Technology)
Jeff Weiss (University of Utah)
Farshid Guilak (Duke University)
Summary (from grant proposal):
Cells of the musculoskeletal system are known to have a biological response to deformation. Deformations, when abnormal in magnitude, duration, and/or frequency content, can lead to cell damage and possible disruption in homeostasis of the extracellular matrix. These mechanisms can be studied in an isolated fashion but connecting mechanical cellular response to organ level mechanics and human movement requires a multiscale approach. At the organ level, physicians perform surgical procedures, investigators try to understand risk of injury, and clinicians prescribe preventive and therapeutic interventions. Many of these operations are aimed at management and prevention of cell damage, and to associate joint level mechanical markers of failure to cell level failure mechanisms. Through human movement, one explores neuromuscular control mechanisms and the influence of physical activity on musculoskeletal tissue properties. At a lower level, mechanical sensation of cell deformations regulate movement control. Physical rehabilitation and exercise regimens are prescribed to promote tissue healing and/or strengthening through cellular regeneration. The knowledge of the mechanical pathway, through which the body level loads are distributed between organs, then within the tissues and further along the extracellular matrix and the cells, is critical for the success of various interventions. However, this information is not established. The goal of this research proposal is to portray that prediction of cell deformations from loads acting on the human body, therefore a clear depiction of the mechanical pathway, is possible, if a multiscale simulation approach is used. Multiresolution models of the knee joint, representative of joint, tissue and cell structure and mechanics, will be developed for this purpose. The knee endures high rates of traumatic injury to its soft tissue structures and it is predominantly affected by osteoarthritis, chronically induced by abnormalities in mechanical loading or how it is transferred to the cartilage. Through multiscale mechanical coupling of these models, a map of cellular deformation in cartilage, ligaments and menisci under a variety of tibiofemoral joint loads will be obtained. Comprehensive mechanical testing at joint, tissue and cell levels will be conducted for parameter estimation and validation, including in vitro loading of the knee joint representative of lifelike loading scenarios. In addition, imaging modalities will capture joint and tissue anatomy, and spatial and deformation related information from cell and extracellular matrix. Advanced computational approaches will be used to obtain model properties and to facilitate multiscale simulations. The approach will combine the expertise of many investigators experienced in biomechanical modeling and experimentation at various biological scales, some with clinical expertise. In future, the research team will utilize this platform to establish the relationship between the structural and loading state of the knee and chondrocyte stresses to explore potential mechanisms of cartilage degeneration. Through documented dissemination of data and models, simulations of other pathologies and translation of the methodology to other organs can be carried out by any interested investigator. | |
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Registered: 2009-07-23 17:33 |
Specimen-Specific Models of the Healthy Knee
- As part of research funded by the National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering (NIBIB), investigators at the University of Denver Center for Orthopaedic Biomechanics have made available a repository of experimental, image, and computational modeling data from mechanical testing of natural human knee biomechanics. It is uncommon for such a comprehensive dataset to be obtained. Therefore, we have made this repository available to assist the greater research community interested in the complexities and pathologies of knee health and mechanical function. Data are provided for 7 human knees (5 cadaveric subjects) and fall under two categories:
Image Data and Experimental & Computational Modeling Data.
Additional details about the data can be found at:
http://ritchieschool.du.edu/research/centers-institutes/orthopaedic-biomechanics/downloads/natural-knee-data/
This repository of natural knee data has been made available thanks to funding from the National Institutes of Health through National Institute of Biomedical Imaging and Bioengineering R01-EB015497. | |
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Registered: 2008-06-12 23:15 |
Reference Models for Multi-Layer Tissue Structures
- This project aims to establish the founding knowledge, data and models for the mechanics of multi-layer tissue structures of the limbs, particularly of the lower and upper legs and arms. The activity is targeted to promote scientific research in layered tissue structures and allow reliable virtual surgery simulations for clinical training and certification.
This research and development project titled “Reference Models for Multi-Layer Tissue Structures" was conducted by the Cleveland Clinic Foundation and was made possible by a contract vehicle which was awarded and administered by the U.S. Army Medical Research & Materiel Command under award number: W81XWH-15-1-0232. The views, opinions and/or findings contained in this website are those of the authors and do not necessarily reflect the views of the Department of Defense and should not be construed as an official DoD/Army position, policy or decision unless so designated by other documentation. No official endorsement should be made. | |
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Registered: 2015-08-24 12:54 |
DeepCell: Deep convolutional neural networks for image segmentation
- The assignment of a cellular identity to individual pixels in microscopy images is a key technical challenge for many live-cell experiments. Traditional approaches to this image segmentation problem have relied on standard computer vision techniques, such as thresholding, morphological operations, and the watershed transform. While these approaches have enabled the analysis of numerous experiments, they are limited in their robustness and in applicability. Here, we show that deep convolutional neural networks, a supervised machine learning method, can robustly segment the cytoplasms of individual bacterial and mammalian cells. This approach automates the analysis of thousands of bacterial cells and leads to more accurate quantification of localization based fluorescent reporters in mammalian cells. In addition, this approach can also simultaneously segment and identify different mammalian cell types in co-cultures. Deep convolutional neural networks have had a transformative impact on the problem of image classification, and we anticipate that they will have a similar impact for live-cell imaging experiments.
Visit our webpage at http://covertlab.github.io/DeepCell | |
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Activity Percentile: 77.10 Registered: 2015-11-16 19:58 |
CoBi Core Models, Data, Training Materials
- This project contains a variety of materials from Computational Biomodeling (CoBi) Core of the Cleveland Clinic, relevant to physics-based simulation of the biomechanical system. These may include various published/unpublished models, data, and training material generated through various small projects. | |
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Registered: 2010-10-07 13:09 |
MB Knee: Multibody Models of the Human Knee
- The purpose of this site is to disseminate geometry and modeling information for development of knee models, primarily in the multibody framework. MBKnee_4 is based on in vivo measurements from a 29 year old female while MBKnee_1, MBKnee_2, and MBKnee_3 are based on cadaver knees that were physically tested in a dynamic knee simulator. Knee geometries (bone, cartilage, and mensici) were derived from Magnetic Resonance Imaging (MRI) and ligament insertions come from MRI, the literature, and probing the cadaver knees. The site also contains information on ligament modeling, such as bundle insertion locations and zero load lengths. Examples of knee models are also provided in the form of ADAMS command files. MBKnee_4 is the most recent model and it includes representation of the medial and lateral menisci, wrapping around bone and cartilage of the meniscal horn attachments, attachments of the deep medial collateral ligament and the anterolateral ligament to the menisci, representation of the posterior oblique ligament and the anterolateral ligament, ligament zero load lengths (or reference strain) determined from experimental laxity measurements, and measured motion to deep flexion.
Funding for this work was provided by the National Institute of Arthritis an Musculoskeletal and Skin Diseases (RAR061698) and by the National Science Foundation (CMS-0506297). | |
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Activity Percentile: 59.16 Registered: 2012-05-25 17:31 |
Evertor and invertor muscle co-activation prevents ankle inversion injury
- The study described in this publication used musculoskeletal simulations to compare the capacity of planned invertor/evertor co-activation versus stretch reflexes with physiologic delay to prevent ankle inversion injuries. To achieve this, developed a novel model, muscle stretch controllers, and muscle reflex controllers for simulating landing in OpenSim. By freely providing the models, software plugins defining the controllers, and the resulting simulations, we hope to enable others to answer questions about landing control and injuries using simulations.
All models, data, and simulation results are provided in the downloads area of this project.
For software and sourcecode defining the novel stretch feedback controller and stretch reflex controller, see the related repository on GitHub.
https://github.com/msdemers/opensim-reflex-controllers
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Activity Percentile: 48.85 Registered: 2015-07-20 20:18 |
Fiber Tractography for Finite-Element Modeling of Transversely Isotropic Tissues
- This project demonstrates the process for fiber tractography of complex biological tissues with transverse isotropy, such as tendon and muscle. This is important for finite element studies of these tissues, as the fiber direction must be specified in the constitutive model. This project contains code, models, and data that can be used to reproduce the results of our publication on this technique. The supplied instructional videos will enable researchers to easily and efficiently apply this method to a variety of other tissues. The software used in the fiber tractography process and demonstrated in this project is Matlab, Autodesk Inventor (free for educators), and Autodesk Simulation CFD (free for educators). Full demonstrations and process instructions can be found in the 7 videos posted at https://vimeo.com/album/3414604:
Contents:
Chapter 1: Introduction (2:35)
This video introduces the CFD fiber tractography software pipeline
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Chapter 2: Supplementary materials code, models and data (20:21)
This video shows the shared models, code, and data posted online at simtk.org/m3lab_cfd4fea.
Chapter 3: Finite element simulations (5:38)
This video shows finite element simulations using the fiber mapping process.
Chapter 4: Iliacus example walkthrough (21:38)
This video shows the step-by-step process for fiber mapping the iliacus muscle (a hip flexor).
Chapter 5: Bflh example walkthrough (12:09)
This video shows the step-by-step process for fiber mapping the biceps femoris longhead muscle (a hamstring).
Chapter 6: Autodesk Inventor segmentation (9:09)
This video shows how to do segmentation of medical images in Autodesk Inventor in order to simplify the solid model for the CFD and FEA software.
Chapter 7: Curved inlet surfaces (6:28)
This video shows how to create curved inlet surfaces for use in Autodesk Simulation CFD. | |
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Activity Percentile: 36.64 Registered: 2015-05-28 18:52 |
Efficient Methods for Multi-Domain Biomechanical Simulations
- This project is an NIH-funded collaboration between the Cleveland Clinic Foundation, the University of Utah, and the Stanford Center for Biomedical Computation (Simbios).
Grant number: 1 R01 EB006735-01
Principal Investigator: Ton van den Bogert
Co-Investigators: Ahmet Erdemir (CCF), Jeff Weiss (University of Utah), and Alan Freed (NASA Glenn Research Center)
Summary (from grant proposal):
In computational biomechanics, there are two well-developed but separate modeling domains: multibody dynamics for body movements, and finite element modeling for tissue deformations. Many clinical problems, however, span both domains. Whole body anatomy, mass distribution, and gait pattern are not typically represented in finite element models, yet these are important real-world factors that affect tissue stresses in the musculoskeletal system, which may contribute to clinical problems such as osteoarthritis and diabetic foot ulceration. Movement simulations, on the other hand, lack a representation of tissue deformations, which are indicators of mechanically induced pain and other sensory feedback (or the lack thereof) and will cause observable changes in gait. Exploration of these neuromusculoskeletal integrative mechanisms can only be accomplished by multi-domain simulations. Current techniques for multi-domain modeling are insufficient because forward dynamic movement simulations typically proceed along a sequence of many small steps in time. Finite element models are too slow to allow a solution at each of these steps. One may painstakingly produce a single movement simulation, but not the thousands of simulations that are required for predictive movement optimizations that are the state of the art in musculoskeletal dynamics. This has become a bottleneck for our own research, as well as for others. Our first aim, therefore, is to implement a generic, self-refining, surrogate modeling scheme, which aims to reproduce an underlying physics-based finite element model within a given error tolerance, but at a far lower computational cost. The self-refining feature is the key to reproduce the multi-dimensional input-output space of a typical finite element model of a joint or joint complex. Our second aim is to demonstrate the utility of these tools by connecting a finite element model of the foot to a complete musculoskeletal gait simulation, which will test the hypothesis that peak plantar pressures (an indicator of diabetic foot ulceration), can be lowered under safety thresholds by selecting a specific optimal muscle coordination pattern during gait. The proposed research will advance the computational environment at the Stanford Center for Biomedical Computation by providing basic surrogate modeling algorithms that are potentially applicable to other multiscale physics-based problems and also extend the Center’s efforts in neuromuscular biomechanics. | |
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Registered: 2006-09-01 17:19 |
Specimen specific finite element model to study cruciate mechanics.
- This project will create a model for the anterior and posterior cruciate ligaments (ACL and PCL)from magnetic resonance imaging (MRI) images. This model will allow users to discover the stresses, strains, and displacements of the ACL and PCL that will result from varying forces applied at different positions on the knee. | |
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Registered: 2014-05-27 18:02 |
Biomechanics of soft tissues in human knee joint
- Abnormal loading of the knee joint could be a result of injuries to the joint tissues like the menisci and ligaments. This subsequently leads to abnormal body weight distribution in the knee joint causing excessive forces in some regions of the joint likely leading to osteoarthritis. It is important to know the functions and relationships that exists between the mechanical properties of the tissues in the knee joint. This work seeks to experimentally characterize the tensile and rupture properties of menisci, cartilage, ligaments and cartilage to determine their strain-, time- and site-specific properties. | |
Activity Percentile: 0.00 Registered: 2014-04-03 17:50 |
Acetaminophen Induced Liver Injury
- The AILI project is a type of In-Silico Liver (ISL) project, which consists of a body of Java code used and reused for exploring hypothetical liver mechanisms. For AILI, the liver mechanisms are those that cause cellular damage, specifically necrosis, because of exposure to acetaminophen. Moreover, the model, a mouse analog, is used for virtual experimentation to explore and explain AILI phenomena, analogous to wet-lab experimentation. A recent addition to this project is studying the disconnect between in vitro and in vivo wet-lab experiments by comparing and contrasting virtual Mouse and Culture Analogs. | |
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Activity Percentile: 0.00 Registered: 2015-05-07 23:25 |
Blood vessel micromechanics
- This project is a parallel finite element analysis (FEA) tool for nonlinear solid mechanics. The FEA tool uses discontinuous Galerkin which specifically designed for nearly incompressible materials such as biological tissue. In addition to the FEA libraries, the project also includes a set of binaries which describe the geometry of the elastin microstructure in rat aorta. The geometry was obtained from high resolution electron microscopy. | |
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Activity Percentile: 0.00 Registered: 2008-09-10 20:37 |
Dynamic Simulation of Joints Using Multi-Scale Modeling
- This research is funded by the National Science Foundation, Grant Number 506297, under the IMAG program for Multiscale Modeling. It is a collaborative effort that capitalizes on a diversity of expertise in areas such as clinical, experimental and computational biomechanics, nano-micro scale material modeling, finite element modeling, and neural networks.
Grant Numer: 506297
Principle Investigator: Trent Guess
Co-Investigators: Ganesh Thiagarajan, Amil Misra, Reza Derakhshani (University of Missouri - Kanas City), Lorin Maletsky (University of Kansas), Terence McIff (University of Kansas Medical Center)
Abstract from grant proposal
Dynamic loading of the knee is believed to play a significant role in the development and progression of tissue wear disease and injury. Macro level rigid body joint models provide insight into joint loading, motion, and motor control. The computational efficiency of these models facilitates dynamic simulation of neuromusculoskeletal systems, but a major limitation is their simplistic (or non-existent) representation of the non-linear, rate dependent behavior of soft tissue structures. This limitation prevents holistic computational approaches to investigating the complex interactions of knee structures and tissues, a limitation that hinders our understanding of the underlying mechanisms of knee injury and disease.
The objective of this project is to develop validated neural network models that reproduce the dynamic behavior of menisci-tibio-femoral articulations and to demonstrate the utility of these models in a musculoskeletal model of the leg. The specific aims of this study are:
Aim 1: Develop finite element (FE) models from micro-structure based constitutive methods that bridge the nano-micro scale behavior at the tissue level
Aim 2: Develop neural network (NN) based models that learn from FE simulation of dynamic behavior of menisci-tibio-femoral articulations
Aim 3: Validate the NN models within a rigid body dynamic model of a natural knee placed within a dynamic knee simulator
Aim 4: Demonstrate the utility of the NN models by placing them within a dynamic musculoskeletal model of the leg to study the interdependencies of the menisci and other knee tissues
Aim 5: Distribute the validated NN models of menisci-tibio-femoral dynamic response and contact pressure for use in any rigid body model of the knee or leg
The final product will be Neural Network (NN) models that conform to a modular application programming interface (API) that can be exported to any commercial integrated development environment (IDE) or in-house multi-body model. The NN models will be built upon a multi-scale approach and describe the non linear, rate dependent, non-homogenous dynamic response of menisci-tibio-femoral articulations in a computationally efficient modular package. The multi-scale modeling approach will be validated using a dynamic knee loading machine and the utility of the approach demonstrated by studying the interdependencies of menisci properties, tibio-femoral contact, and anterior cruciate ligament strain during a dual limb squat. A synergistic interdisciplinary team has been assembled to address the objective and aims of the proposed project comprising experts in rigid body dynamics and knee modeling, FE modeling, nano-micro scale material modeling, neural networks, and clinical and experimental biomechanics.
The proposed research will benefit society at large as the results of this work have potential applications to orthopedics, tissue engineering, and biomaterials. The work will also be a valuable asset to the musculoskeletal research community providing computational tools that may aid research in broad areas such as human movement, prosthetics, tissue engineering, sport injury, and disease. | |
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Registered: 2006-10-18 19:49 |
Continuity: A Problem-Solving Environment for Multi-Scale Biology
- Continuity 6 is a problem-solving environment for multi-scale modeling and data analysis in bioengineering and physiology, especially finite element modeling in cardiac biomechanics, biotransport and electrophysiology. Continuity 6 is distributed free for academic research by the National Biomedical Computation Resource (NBCR). Continuity 6 is portable, object-oriented, has separate GUI client and computational server components, and runs under Windows, MacOS or Linux, including Linux clusters. | |
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Activity Percentile: 0.00 Registered: 2009-03-19 22:44 |