Project Tree
Now limiting view to projects in the following categories:
All Topics :: Biocomputational Focus :: Physics-Based Simulation [Remove This Filter]
All Topics > Primary Content > Data Sets |
Browse By: |
51 projects in result set. Displaying 20 per page. Projects sorted by alphabetical order.
<1> <2> <3>
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>.
| |
|
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). | |
|
Registered: 2018-11-28 20:40 |
SimVascular: Examples and Clinical Cases
- We invite you to download and try these examples and clinical case projects, which are all compatible with the open source SimVascular cardiovascular modeling software package. Each case includes image data of a healthy or diseased individual, a 3D anatomic model created from the image data, and simulation job files which specify initial conditions, boundary conditions and various parameters required to run the simulation. Many of the cases are already organized as SV projects, which means you can easily load them into SimVascular and view or try out various project components. Following the guides in the SimVascular documentation website, you can also create new models and run simulations with different conditions, based on these example cases.
You are free to download the examples and cases provided that you properly reference the source. The cases are part of the academic output of the researcher cited and should be referred to as such. Permission is granted to use these cases for research purposes, but for commercial use please contact the director of the Cardiovascular Biomechanics Computation Lab, Alison Marsden (amarsden@stanford.edu).
The examples and clinical cases included are:
Example: Demo Project
Example: Cylinder Project (no image, for simulation)
Clinical Case: Coronary Normal
Clinical Case: Aortofemoral Normal 1
Clinical Case: Aortofemoral Normal 2
Clinical Case: Healthy Pulmonary
SimVascular is available for download at our project website at:
https://simtk.org/projects/simvascular
Comprehensive documentation is available on the SimVascular website at:
http://www.simvascular.org
| |
|
Activity Percentile: 95.04 Registered: 2014-03-14 20:12 |
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. | |
|
Activity Percentile: 94.66 Registered: 2009-07-14 23:24 |
Are subject-specific musculoskeletal models robust to parameter identification?
- This study analyzed the sensitivity of the predictions of an MRI-based musculoskeletal model (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the unavoidable uncertainties in parameter identification, i.e., body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. | |
|
Activity Percentile: 92.37 Registered: 2014-11-10 15:19 |
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. | |
|
Registered: 2009-07-23 17:33 |
Model of the Scapulothoracic Joint
- In this study, we developed a rigid-body model of a scapulothoracic joint to describe the kinematics of the scapula relative to the thorax. This model describes scapula kinematics with four degrees of freedom: 1) elevation and 2) abduction of the scapula on an ellipsoidal thoracic surface, 3) upward rotation of the scapula normal to the thoracic surface, and 4) internal rotation of the scapula to lift the medial border of the scapula off the surface of the thorax. The surface dimensions and joint axes can be customized to match an individual’s anthropometry. We compared the model to “gold standard” bone-pin kinematics collected during three shoulder tasks and found modeled scapula kinematics to be accurate to within 2 mm root-mean-squared error for individual bone-pin markers across all markers and movement tasks. As an additional test, we added random and systematic noise to the bone-pin marker data and found that the model reduced kinematic variability due to noise by 65% compared to Euler angles computed without the model. Our scapulothoracic joint model can be used for inverse and forward dynamics analyses and to compute joint reaction loads. The computational performance of the scapulothoracic joint model is well suited for real-time applications, is freely available as an OpenSim 3.2 plugin, and is customizable and usable with other OpenSim models. | |
|
Activity Percentile: 88.17 Registered: 2015-01-14 23:10 |
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. | |
|
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. | |
|
Registered: 2015-08-24 12:54 |
Simulation of Constrained Musculoskeletal Systems in Task Space
- Objective: This work proposes an operational task space formalization of constrained musculoskeletal systems, motivated by its promising results in the field of robotics.
Methods: The change of representation requires different algorithms for solving the inverse and forward dynamics simulation in the task space domain. We propose an extension to the Direct Marker Control and an adaptation of the Computed Muscle Control algorithms for solving the inverse kinematics and muscle redundancy problems respectively.
Results: Experimental evaluation demonstrates that this framework is not only successful in dealing with the inverse dynamics problem, but also provides an intuitive way of studying and designing simulations, facilitating assessment prior to any experimental data collection.
Significance: The incorporation of constraints in the derivation unveils an important extension of this framework towards addressing systems that use absolute coordinates and topologies that contain closed kinematic chains. Task space projection reveals a more intuitive encoding of the motion planning problem, allows for better correspondence between observed and estimated variables, provides the means to effectively study the role of kinematic redundancy and, most importantly, offers an abstract point of view and control, which can be advantageous towards further integration with high level models of the precommand level.
Conclusion: Task-based approaches could be adopted in the design of simulation related to the study of constrained musculoskeletal systems.
The source code of the project can be found at: https://github.com/mitkof6/opensim-task-space.git
The new API of task space and constraint projection for OpenSim V4.0 is available at: https://github.com/mitkof6/task-space
<iframe width="560" height="315" src="https://www.youtube.com/embed/jfE14iWRZDs" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe> | |
|
Registered: 2017-08-28 12:06 |
Validation of Subject-Specific Knee Models from In Vivo Measurements
- Working models, data, code, and results for work to validate that in vivo methods for measuring laxity and obtaining knee geometry are comparable to methods previously standard for knee modeling of in vitro specimens.
This dataset is part of an ongoing manuscript to validate that sources of data from currently available in vivo methods are sufficient to create computational models of the knee compared with existing in vitro techniques. The data included in this repository is for the S192803 specimen of that dataset and includes experimental data, working models, code, and results obtained for that model and used in that manuscript.
The dataset contains experimental data, models, code, and results for the S192803 specimen data. This dataset is one of two model datasets used in the paper Validation of Subject-Specific Knee Models from In Vivo Measurements, which is in review at the Journal of Biomechanical Engineering. The dataset contained herein is derived from the experimental data collected during a previous publication in the Journal of Medical Devices, entitled: "Apparatus for In Vivo Knee Laxity Assessment Using High-Speed Stereo Radiography". Available at: https://doi.org/10.1115/1.4051834
A similar dataset exists for the other specimen, S193761.
Work was created by Dr. Thor E. Andreassen, Dr. Donald R. Hume, Dr. Landon D. Hamilton, Stormy L. Hegg, Sean E. Higinbotham, and Dr. Kevin B. Shelburne at the Center for Orthopaedic Biomechanics at the University of Denver.
The work was funded by the NIH National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institute of Biomedical Imaging and Bioengineering, and the National Institute of Child Health and Human Development (Grant U01 AR072989).
If you have any questions, please email the main author, Dr. Thor Andreassen, at thor.andreassen@du.edu
Sharing/USE
This Code/Software is free to use for any reason. However, we ask that if you use any part of this work, that you cite the original two works that made it possible:
Andreassen, T. E., Hamilton, L. D., Hume, D., Higinbotham, S. E., Behnam, Y., Clary, C., and Shelburne, K. B. (September 10, 2021). "Apparatus for In Vivo Knee Laxity Assessment Using High-Speed Stereo Radiography." ASME. J. Med. Devices. December 2021; 15(4): 041004. https://doi.org/10.1115/1.4051834
Andreassen, T. E., Hume, D. R., Hamilton, L. D., Hegg, S.L., Higinbotham, S. E., and Shelburne, K. B. "Validation of Subject-Specific Knee Models from In Vivo Measurements." ASME. J. Biomech, Engineering.
Liability Agreement
The Data is provided “as is” with no express or implied warranty or guarantee. The University of Denver and the Center for Orthopaedic Biomechanics do not accept any liability or provide any guarantee in connection with uses of the Data, including but not limited to, fitness for a particular purpose and noninfringement. The University of Denver and the Center for Orthopaedic Biomechanics are not liable for direct or indirect losses or damage, of any kind, which may arise through the use of this data. | |
Registered: 2023-12-12 23:13 |
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. | |
|
Registered: 2010-10-07 13:09 |
Simulated Assistive Devices for Loaded Walking
- This project contains experimental data and muscle-actuated tracking simulations of male subjects walking while carrying heavy load, and OpenSim simulations of these subjects wearing hypothetical ideal assistive devices. We collected motion capture data of 7 subjects walking in 4 different conditions: walking (a) without load at a freely selected speed, (b) without load at 80% of the freely selected speed, (c) while carrying 38 kg on the torso at a new freely selected speed, and (d) while carrying 38 kg at the same speed as in (a).
Based on the simulations of loaded walking (condition (c) above), we created new simulations to predict the effect of ideal assistive devices on the metabolic cost of walking. We examined 7 massless devices that each provided unrestricted torque at one degree of freedom and in one direction: hip abduction, hip flexion, hip extension, knee flexion, knee extension, ankle plantarflexion, and ankle dorsiflexion. We estimated the optimal device torques, and the devices' effect on metabolic cost and muscle activity.
Dembia CL, Silder A, Uchida TK, Hicks JL, Delp SL (2017) Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads. PLoS ONE 12(7): e0180320. https://doi.org/10.1371/journal.pone.0180320 | |
|
Registered: 2016-05-07 02:08 |
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
<object width="560" height="500"><param name="movie" value="//www.youtube.com/v/6OkaTvEmpWk?hl=en_US&version=3"></param><param name="allowFullScreen" value="true"></param><param name="allowscriptaccess" value="always"></param><embed src="//www.youtube.com/v/6OkaTvEmpWk?hl=en_US&version=3" type="application/x-shockwave-flash" width="560" height="500" allowscriptaccess="always" allowfullscreen="true"></embed></object> | |
|
Activity Percentile: 48.85 Registered: 2015-07-20 20:18 |
Simulating Ideal Assistive Devices to Reduce the Metabolic Cost of Running
- We used simulation to predict and gain insight into the biomechanical and energetic effects of assisted running, and to demonstrate the potential for simulation to complement experimental approaches to device design. We performed muscle-driven simulations of running at 2 and 5 m/s, then added ideal, massless assistive devices and examined the predicted changes in muscle recruitment patterns and metabolic power consumption. We predicted the optimal assistive device torque profiles and sought explanations for the observed changes in muscle activity. By ignoring device mass and other practical factors, we avoided confounding the beneficial effects of adding assistance with the detrimental side effects often encountered experimentally. | |
|
Activity Percentile: 45.80 Registered: 2016-02-24 01:34 |
Predictive Simulation of Loaded and Inclined Walking
- The studies described on this site use optimal control techniques to synthesize biomechanically accurate humanoid motion for walking. The techniques described have been demonstrated to yield comparable results to experimental walking data. | |
|
Registered: 2012-02-17 03:21 |
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
<!-- This version of the embed code is no longer supported. Learn more: https://vimeo.com/s/tnm --> <object width="500" height="281"><param name="allowfullscreen" value="true" /><param name="allowscriptaccess" value="always" /><param name="movie" value="https://vimeo.com/moogaloop.swf?clip_id=129107314&force_embed=vimeo.com&fullscreen=1" /><embed src="https://vimeo.com/moogaloop.swf?clip_id=129107314&force_embed=vimeo.com&fullscreen=1" type="application/x-shockwave-flash" allowfullscreen="true" allowscriptaccess="always" width="500" height="281"></embed></object>
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. | |
|
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. | |
|
Registered: 2006-09-01 17:19 |
Normal human left ventricular myofiber stress
- Ventricular wall stress is believed to be responsible for many physical mechanisms taking place in the human heart, including ventricular remodeling, which is frequently associated with heart failure. Therefore, normalization of ventricular wall stress is the cornerstone of many existing and new treatments for heart failure. In this paper, we sought to construct reference maps of normal ventricular wall stress in humans that could be used as a target for in silico optimization studies of existing and potential new treatments for heart failure. To do so, we constructed personalized computational models of the left ventricles of five normal human subjects using magnetic resonance images and the finite element method. These models were calibrated using left ventricular volume data extracted from magnetic resonance imaging (MRI) and validated through comparison with strain measurements from tagged MRI (950 ± 170 strain comparisons/subject). The calibrated passive material parameter values were C0 = 0.115 ± 0.008 kPa and B0 = 14.4 ± 3.18; the active material parameter value was Tmax = 143 ± 11.1 kPa. These values could serve as a reference for future construction of normal human left ventricular computational models. The differences between the predicted and the measured circumferential and longitudinal strains in each subject were 3.4% ± 6.3% and 0.5% ± 5.9%, respectively. The predicted end-diastolic and end-systolic myofiber stress fields for the five subjects were 2.21 ± 0.58 kPa and 16.54 ± 4.73 kPa, respectively. Thus, these stresses could serve as targets for in silico design of heart failure treatments. | |
|
Registered: 2014-06-04 18:58 |
How Tendon Compliance Affects the Metabolic Cost of Running
- We investigated the effect of tendon compliance on the metabolic cost of running using a full-body musculoskeletal model with a detailed model of muscle energetics. We performed muscle-driven simulations of running at several speeds and tendon compliances, and computed the average metabolic power consumed by each muscle. We used modeling and simulation to gain insight into the energy consumed by individual muscles throughout the gait cycle. We compared trends observed in muscle activations, metabolic power, and fiber mechanical power over a broad range of tendon compliances and at four running speeds. | |
|
Activity Percentile: 29.39 Registered: 2016-01-06 21:36 |
51 projects in result set. Displaying 20 per page. Projects sorted by alphabetical order.
<1> <2> <3>