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30 projects in result set. Displaying 20 per page. Projects sorted by alphabetical order.
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Practical Annotation and Exchange of Virtual Anatomy
- Representation of anatomy in a virtual form is at the heart of clinical decision making, biomedical research, and medical training. Virtual anatomy is not limited to description of geometry but also requires appropriate and efficient labeling of regions - to define spatial relationships and interactions between anatomical objects; effective strategies for pointwise operations - to define local properties, biological or otherwise; and support for diverse data formats and standards - to facilitate exchange between clinicians, scientists, engineers, and the general public. Development of aeva, a free and open source software package (library, user interfaces, extensions) capable of automated and interactive operations for virtual anatomy annotation and exchange, is in response to these currently unmet requirements. This site serves for aeva outreach, including dissemination the software and use cases. The use cases drive design and testing of aeva features and demonstrate various workflows that rely on virtual anatomy.
aeva downloads:
Downloads (https://simtk.org/frs/?group_id=1767)
Kitware data repository (https://data.kitware.com/#folder/5e7a4690af2e2eed356a17f2)
aeva documentation:
Guides and tutorials (https://aeva.readthedocs.io)
aeva videos:
Short instructions (https://www.youtube.com/channel/UCubfUe40LXvBs86UyKci0Fw)
aeva source code:
Kitware source code repository (https://gitlab.kitware.com/aeva)
aeva forum:
Forums (https://simtk.org/plugins/phpBB/indexPhpbb.php?group_id=1767 ) | |
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Registered: 2019-08-28 01:27 |
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. | |
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Activity Percentile: 92.75 Registered: 2014-11-10 15:19 |
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. | |
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Activity Percentile: 89.31 Registered: 2015-01-14 23:10 |
SCONE: Open Source Software for Predictive Simulation
- If SCONE is helpful for your research, please cite the following paper:
Geijtenbeek, T (2019). SCONE: Open Source Software for Predictive Simulation of Biological Motion. Journal of Open Source Software, 4(38), 1421, https://doi.org/10.21105/joss.01421 | |
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Registered: 2016-10-27 13:07 |
BlurLab -- 3D Microscopy Simulation Package
- BlurLab is an easy to use platform for generating simulated fluorescence microscopy data for use in mechanistic modeling visualization, image comparison, and hypothesis testing. The software accepts the 3D positions, intensities and labels of fluorescing objects that are produced by an underlying mechanistic model and transforms them into high quality simulated images. The program includes full 3D convolution with realistic (or even measured) point spread functions; inclusion of thermal, shot and custom noise spectra; simulations of mean and fully stochastic photobleacing; the ability to view scenes in wide-field and TIRF, and perform Z-slicing; and the ability to simulate FRAP experiments.
The software provides a platform for adjusting and saving these simulated images, as well as a number of helpful, semi-automated features to make image simulation easy and less error prone. | |
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Activity Percentile: 79.01 Registered: 2011-08-05 01:17 |
A three-dimensional musculoskeletal model of the dog
- The domestic dog is interesting to investigate because of their wide range of body size, body mass, and physique. In the last several years, the number of clinical and biomechanical studies on dog locomotion has increased. However, the relationship between body structure and joint load during locomotion, as well as between joint load and degenerative diseases of the locomotor system (e.g. dysplasia), are not sufficiently understood. Collecting this data through in vivo measurements/records of joint forces and loads on deep/small muscles is complex, invasive, and sometimes unethical. The use of detailed musculoskeletal models may help fill the knowledge gap. We describe here the methods we used to create a detailed musculoskeletal model with 84 degrees of freedom and 134 muscles. Our model has three key-features: three-dimensionality, scalability, and modularity. We tested the validity of the model by identifying forelimb muscle synergies of a beagle at walk. We used inverse dynamics and static optimization to estimate muscle activations based on experimental data. We identified three muscle synergy groups by using hierarchical clustering. The activation patterns predicted from the model exhibit good agreement with experimental data for most of the forelimb muscles. We expect that our model will speed up the analysis of how body size, physique, agility, and disease influence neuronal control and joint loading in dog locomotion. | |
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Registered: 2020-11-30 08:11 |
Simbody: Multibody Physics API
- This project is a SimTK toolset providing general multibody dynamics capability, that is, the ability to solve Newton's 2nd law F=ma in any set of generalized coordinates subject to arbitrary constraints. (That's Isaac himself in the oval.) Simbody is provided as an open source, object-oriented C++ API and delivers high-performance, accuracy-controlled science/engineering-quality results.
Simbody uses an advanced Featherstone-style formulation of rigid body mechanics to provide results in Order(<em>n</em>) time for any set of <em>n</em> generalized coordinates. This can be used for internal coordinate modeling of molecules, or for coarse-grained models based on larger chunks. It is also useful for large-scale mechanical models, such as neuromuscular models of human gait, robotics, avatars, and animation. Simbody can also be used in real time interactive applications for biosimulation as well as for virtual worlds and games.
This toolset was developed originally by Michael Sherman at the Simbios Center at Stanford, with major contributions from Peter Eastman and others. Simbody descends directly from the public domain NIH Internal Variable Dynamics Module (IVM) facility for molecular dynamics developed and kindly provided by Charles Schwieters. IVM is in turn based on the spatial operator algebra of Rodriguez and Jain from NASA's Jet Propulsion Laboratory (JPL), and Simbody has adopted that formulation.
<b>SOURCE CODE:</b> Simbody is distributed in source form. The source code is maintained at <a href="https://www.github.com/simbody">GitHub</a>. You can get a zip of the latest stable release <a href="https://github.com/simbody/simbody/releases">here</a>, then build it on your Windows, Mac OSX, or Linux machine (you will need CMake and a compiler).
You can also clone the git repository and build the latest development version <a href="https://github.com/simbody/simbody">here</a>; the repository URL is https://github.com/simbody/simbody.git. If you would like to contribute bug fixes, new code, documentation, examples, etc. to Simbody (and we hope you will!), please fork the repository on GitHub and send pull requests.
If you are new to git, you may want to start with GitHub's <a href="https://help.github.com/categories/54/articles">Bootcamp tutorial</a>. | |
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Registered: 2005-07-26 19:52 |
Studying Anterior Cruciate Ligament Strains in Young Female Athletes
- The central goal of this study is to contribute toward advancements made in determining the underlying causes of anterior cruciate ligament (ACL) injuries in young female athletes performing high impact activities like stop jumps. ACL injuries are frequently incurred by recreational and professional young female athletes during non-contact impact activities in sports like volleyball and basketball. This musculoskeletal-neuromuscular study investigated stop jumps and factors related to ACL injury like knee valgus and internal–external rotations and moment loads, as well as ACL strains and internal forces. The dynamic simulation steps undertaken for this analysis using OpenSim 3.2 include Model Scaling, Inverse Kinematics, Residual Reduction, Computed Muscle Control and Forward Dynamics. | |
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Activity Percentile: 52.29 Registered: 2014-08-05 18:24 |
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: 24.05 Registered: 2015-05-28 18:52 |
Muscle function of overground running across a range of speeds
- This project is a repository of overground running data (3.5m/s 5.2m/s, 7.0m/s and 9.0m/s) along with a working musculoskeletal model to perform simulations and derive the function of individual muscles. | |
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Registered: 2011-08-07 14:01 |
Neuromuscular Models for the Predictive Treatment of Parkinson's Disease
- NoTremor aims to provide patient specific computational models of the coupled brain and neuromuscular systems that will be subsequently used to improve the quality of analysis, prediction and progression of Parkinson’s disease. In particular, it aspires to establish the neglected link between brain modelling and neuromuscular systems that will result in a holistic representation of the physiology for PD patients. | |
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Activity Percentile: 6.87 Registered: 2014-06-18 13:56 |
The Osteoporotic Virtual Physiological Human
- Nearly four million osteoporotic bone fractures cost the European health system more than 30 billion Euro per year. This figure could double by 2050. After the first fracture, the chances of having another one increase by 86%. We need to prevent osteoporotic fractures. The first step is an accurate prediction of the patient-specific risk of fracture that considers not only the
skeletal determinants but also the neuromuscular condition. The aim of VPHOP is to develop a multiscale modelling technology based on conventional diagnostic imaging methods that makes it possible, in a clinical setting, to predict for each patient the strength of his/her bones, how this strength is likely to change over time, and the probability that the he/she will overload his/her bones during daily life. With these three predictions, the evaluation of the
absolute risk of bone fracture will be much more accurate than any prediction based on
external and indirect determinants, as it is current clinical practice. These predictions will be used to: i) improve the diagnostic accuracy of the current clinical standards; ii) to provide the basis for an evidence-based prognosis with respect to the natural evolution of the disease, to pharmacological treatments, and/or to preventive interventional treatments aimed to selectively strengthen particularly weak regions of the skeleton. For patients at high risk of fracture, and for which the pharmacological treatment appears insufficient, the VPHOP system will also assist the interventional radiologist in planning the augmentation procedure.
The various modelling technologies developed during the project will be validated not only in vitro, on animal models, or against retrospective clinical outcomes, but will also be assessed in term of clinical impact and safety on small cohorts of patients enrolled at four different clinical institutions, providing the factual basis for effective clinical and industrial exploitations. | |
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Registered: 2010-03-08 08:57 |
CT-scan based extended dynamic foot model
- This project aims to develop a more realistic dynamic foot model, capturing the full complexity of the foot biomechanics. This model is constructed semi-automatically using CT images. In addition intrinsic foot muscles and ligaments were added. | |
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Activity Percentile: 0.00 Registered: 2015-06-24 08:40 |
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 |
FEBio: Finite Elements for Biomechanics
- FEBio is a nonlinear finite element solver that is specifically designed for biomechanical applications. It offers modeling scenarios, constitutive models, and boundary conditions that are relevant to many research areas in biomechanics and biophysics. All features can be used together seamlessly, giving the user a powerful tool for solving 3D problems in computational biomechanics. The software is open-source, and pre-compiled executables for Windows, Mac OS X and Linux platforms are available.
Current modeling capabilities include:
* Large deformation quasi-static and dynamic structural mechanics analysis.
* Modeling of complex structures that contain a combination of deformable and rigid parts.
* Multiphasic modeling, where the solvent can contain any number of solutes that may undergo chemical reactions.
* Fluid mechanics analysis, both steady-state and transient
* Fluid-solid interaction (FSI), which combines the powerful solid and fluid solvers.
FEBio also supports a plugin framework that can be used to easily develop new features for FEBio, including new constitutive models, boundary conditions, and even entire new physics solvers.
For more information check out the FEBio website at http://www.febio.org | |
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Registered: 2007-09-14 16:08 |
Prediction of trunk muscle size and position
- This project provides programs for predicting trunk muscle size and position values given sex, age, height, and weight. The predictions apply regressions developed based on CT measurements in a multi-ethnic sample of the Framingham Heart Study. This implements the regressions in Python code, specifically enabling users to run online via Google Colab notebooks. This may be of interest for researchers creating musculoskeletal models or other studies needing estimates of muscle morphometry.
The code (available in downloads) provides estimations of trunk muscle cross-sectional areas and positions at the vertebral levels between T4 and L4. Copy the Google Colab notebook to your Google Colab account to run. https://colab.research.google.com/
The form requires submission of a subject's sex, weight ( kg / lb ), height ( cm / in ), Age and ID (optional). After clicking the play button on the form an excel workbook will be downloaded. It contains four sheets: Cross-Sectional Area in mm^2 , Distance in mm^2 , Angle in degrees and an additional sheet describing your subject's inputs. Note that this calculator was developed with a dataset containing healthy adults between ages 40 and 90. Application outside this range may not be accurate, and this should not be used for children and adolescents.
FAQ.
1: What sample pool was used to generate the regression used in this calculator?
Table 1: Mean (SD) [Range] characteristics of participants included in the sample.
<table border="1"> <tr> <th style="background-color: gray"> </th> <th style="background-color: gray"> Men (N=247) </th> <th style="background-color: gray"> Women (N=260 ) </th> </tr> <tr> <th style="background-color: gray">Age (years)</th> <td>60.8 (14.1) [40-88]</td> <td>61.8 (12.6) [40-90]</td> </tr> <tr> <th style="background-color: gray">Height (cm)</th> <td>173.8 (7.2) [155.5-193.7] </td> <td>159.9 (6.6) [139.7-175.9] </td> </tr> <tr> <th style="background-color: gray"> Weight (kg)</th> <td>86.0 (14.4) [47.2-122.9]</td> <td>70.7 (15.3) [40.4-127.0]</td> </tr>
</table>
2: Can this calculator be used for anyone?
The calculator can be used for anyone who falls within the data ranges noted above (i.e age 40 – 90, 140cm-195cm (4ft-6.4ft) and 70kg -130kg (154lbs-286lbs). Outside these ranges, the calculator may still be used, but will generate a warning that predictions are being extrapolated. Prediction intervals will also increase as inputs move outside the range of the sample. If an age < 40 is entered, the calculations will be performed for age = 40, as aging-related effects are likely not found in the same way for adults under 40.
3: How were the muscle distance and angle measurements calculated?
Measurements were performed in transverse plane CT scans at the mid-level of the vertebral body. After segmenting a muscle, the CSA is defined as its area in this plane. The distance and angle refer to the transverse plane polar coordinates representing position of a muscle’s centroid in relation to the centroid of the vertebral body, where the posterior direction is 0°.
4. What are the prediction intervals?
The prediction intervals are calculated at each vertebral level along with the predicted value. The prediction intervals for an outcome (lower 95% and upper 95%) provide a likely range of values for an individual with the given input sex, age, height and weight. A hard lower limit of 0 is applied for CSA predictions and distance predictions, and lower and upper limits of 0° and 180°, respectively, for angle predictions.
5. How do I run multiple individuals at once?
Use the MuscleCalculator_ForBulkUse. pynb code and fill in the arrays with your individuals' information, using commas for delineation and quotation marks for Sex, WeightUnits, HeightUnits and Names. Click run and your files will download. Your browser may ask you to allow multiple files to be downloaded. If you do not see a notification for the files being downloaded, check your google drive folder as some browsers may automatically send it there.
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Registered: 2022-12-09 18:54 |
C++ and Python code, distributed computing and OpenMM interfaces for simulations
- please cite: "Interplay of Protein and DNA Structure Revealed in Simulations of the lac Operon" (PLOS One 2013)
for any code related to protein-DNA modeling and
"Free Energy Monte Carlo Simulations on a Distributed Network" (Lecture Notes in Computer Science Journal for PARA 2010)
http://link.springer.com/chapter/10.1007%2F978-3-642-28145-7_1
for parallel client-server code, users of additional code should cite this web site. Code is provided as-is with no warranty and examples are provided to illustrate the usage of these modeling techniques with some sample systems. Code is the intellectual property of Luke Czapla, developer and biophysicist. Examples are provided in C/C++ and Python. | |
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Activity Percentile: 0.00 Registered: 2014-02-01 22:32 |
Predictive framework for functional electrical stimulation (FES) cycling
- Enhancing the efficacy of spinal cord injury (SCI) rehabilitation is crucial for a patient’s optimal recovery. While functional electrical stimulation (FES) cycling stands as a standard therapy, achieving notable improvements proves challenging due to the inherent complexities embedded in the dynamics of the movement. Indeed, overcoming the time-consuming nature of cycling becomes imperative, prompting the development of predictive models through optimal control simulation. The current challenge lies in the demand for a specific framework that considers the unique intricacies of SCI FES cycling. In response, our innovative approach introduces a novel framework and showcases its application in solving predictive models. Leveraging open-source tools, including OpenSim and Blender, we built the FES cycling model. Subsequently, we outlined predictive problems within OpenSim Moco. This advancement mitigates the time-consuming constraints of prior methods. This improved avenue for simulating FES cycling for SCI rehabilitation paves the way for practical and time-effective integration of Digital Twins in clinical applications. | |
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Registered: 2018-07-18 14:14 |
Biomechanics in Product Design
- This project provides methods and tools to conduct biomechanical analyses of human-artifact interaction. The objective is to facilitate the creation of models to predict the effects of interaction processes on the human musculoskeletal system. Designers gain valuable insights on how to improve the ergonomic quality of consumer products and workplaces. Our research currently focuses on the integration of OpenSim into CAD environments as well as the prediction of human motion based on interaction goals. | |
Activity Percentile: 0.00 Registered: 2014-07-02 08:11 |
Purkinje Network Generation with Fractal Trees
- This project is a tool to generate Purkinje networks in realistic representations of the ventricles. Using fractal trees, our method provides an anatomically based approximation to the network. The input consist of a surface discretized with triangles and the output consist of a finite element mesh, suitable for simulations.
The source code is available in this repository and in <a href="https://www.github.com/fsahli/fractal-tree/">GitHub</a>. The documentation can be found in <a href="https://fractal-tree.readthedocs.org">fractal-tree.readthedocs.org</a>. | |
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Activity Percentile: 0.00 Registered: 2015-11-25 00:10 |
30 projects in result set. Displaying 20 per page. Projects sorted by alphabetical order.
<1> <2>