computational biomechanical models Search Results


computational biomechanical models  (OpenSim Ltd)
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OpenSim Ltd computational biomechanical models
Computational Biomechanical Models, supplied by OpenSim Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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computational biomechanical models - by Bioz Stars, 2026-03
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computational cardiac biomechanics model  (Dassault Systemes)
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Dassault Systemes computational cardiac biomechanics model
Workflow of the data assimilation and personalization process for a left-ventricular <t>biomechanics</t> model of one porcine heart. I) (orange box) Ventricular and aortic catheter pressure measurements are combined and smoothed. The diastolic pressure trace and the maximal systolic pressure value are extracted to personalize a template pressure curve extracted from the underlying Living Heart Model (LHM), while keeping the duration of diastole, systolic pressure increase, and pressure decrease of the template pressure curve constant. This pressure trace provides the hemodynamic boundary condition for the simulation (orange input). II) (red box) The volume over the cardiac cycle is extracted from cine images using tracking. The mesh extracted in diastasis is used as initial state for the simulation. End-diastolic and diastatic volumes are used to adapt the passive model parameters by scaling the parameter A (purple) and to estimate an unloaded reference state. The end-systolic frame is used to adapt the active model (green). The end-systolic volume is used to fit the maximal active tension parameter T max , the left-ventricular length of this frame is used to adapt the parameter n , scaling the active stress contribution in sheet direction. III) (blue box) The predominant aggregated myocyte orientation is measured with in-vivo cDTI in eight short-axis slices. Four interpolation techniques are applied to obtain a 3D fiber field on the 3D mesh. The sheet direction is estimated to reach a diastolic E2A angle of 13°. The biomechanics model uses the LHM implementation of the passive orthotropic Holzapfel and Ogden material model and the active Guccione model . The reference geometry is estimated by unloading the initial state using a suction problem and subsequent loading to end-diastole. This procedure (purple arrows) is repeated while iterating over the passive material scaling (A). The volume after loading is compared to the data at the loading condition of the initial diastatic pressure and at end-diastole. The sum of these volume errors serves as objective function to identify the unloaded reference and passive scaling parameter estimates. The simulation over the cardiac cycle is performed starting from the unloaded reference (black arrows).
Computational Cardiac Biomechanics Model, supplied by Dassault Systemes, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Average 90 stars, based on 1 article reviews
computational cardiac biomechanics model - by Bioz Stars, 2026-03
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Image Search Results


Workflow of the data assimilation and personalization process for a left-ventricular biomechanics model of one porcine heart. I) (orange box) Ventricular and aortic catheter pressure measurements are combined and smoothed. The diastolic pressure trace and the maximal systolic pressure value are extracted to personalize a template pressure curve extracted from the underlying Living Heart Model (LHM), while keeping the duration of diastole, systolic pressure increase, and pressure decrease of the template pressure curve constant. This pressure trace provides the hemodynamic boundary condition for the simulation (orange input). II) (red box) The volume over the cardiac cycle is extracted from cine images using tracking. The mesh extracted in diastasis is used as initial state for the simulation. End-diastolic and diastatic volumes are used to adapt the passive model parameters by scaling the parameter A (purple) and to estimate an unloaded reference state. The end-systolic frame is used to adapt the active model (green). The end-systolic volume is used to fit the maximal active tension parameter T max , the left-ventricular length of this frame is used to adapt the parameter n , scaling the active stress contribution in sheet direction. III) (blue box) The predominant aggregated myocyte orientation is measured with in-vivo cDTI in eight short-axis slices. Four interpolation techniques are applied to obtain a 3D fiber field on the 3D mesh. The sheet direction is estimated to reach a diastolic E2A angle of 13°. The biomechanics model uses the LHM implementation of the passive orthotropic Holzapfel and Ogden material model and the active Guccione model . The reference geometry is estimated by unloading the initial state using a suction problem and subsequent loading to end-diastole. This procedure (purple arrows) is repeated while iterating over the passive material scaling (A). The volume after loading is compared to the data at the loading condition of the initial diastatic pressure and at end-diastole. The sum of these volume errors serves as objective function to identify the unloaded reference and passive scaling parameter estimates. The simulation over the cardiac cycle is performed starting from the unloaded reference (black arrows).

Journal: Frontiers in Physiology

Article Title: Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods

doi: 10.3389/fphys.2022.1042537

Figure Lengend Snippet: Workflow of the data assimilation and personalization process for a left-ventricular biomechanics model of one porcine heart. I) (orange box) Ventricular and aortic catheter pressure measurements are combined and smoothed. The diastolic pressure trace and the maximal systolic pressure value are extracted to personalize a template pressure curve extracted from the underlying Living Heart Model (LHM), while keeping the duration of diastole, systolic pressure increase, and pressure decrease of the template pressure curve constant. This pressure trace provides the hemodynamic boundary condition for the simulation (orange input). II) (red box) The volume over the cardiac cycle is extracted from cine images using tracking. The mesh extracted in diastasis is used as initial state for the simulation. End-diastolic and diastatic volumes are used to adapt the passive model parameters by scaling the parameter A (purple) and to estimate an unloaded reference state. The end-systolic frame is used to adapt the active model (green). The end-systolic volume is used to fit the maximal active tension parameter T max , the left-ventricular length of this frame is used to adapt the parameter n , scaling the active stress contribution in sheet direction. III) (blue box) The predominant aggregated myocyte orientation is measured with in-vivo cDTI in eight short-axis slices. Four interpolation techniques are applied to obtain a 3D fiber field on the 3D mesh. The sheet direction is estimated to reach a diastolic E2A angle of 13°. The biomechanics model uses the LHM implementation of the passive orthotropic Holzapfel and Ogden material model and the active Guccione model . The reference geometry is estimated by unloading the initial state using a suction problem and subsequent loading to end-diastole. This procedure (purple arrows) is repeated while iterating over the passive material scaling (A). The volume after loading is compared to the data at the loading condition of the initial diastatic pressure and at end-diastole. The sum of these volume errors serves as objective function to identify the unloaded reference and passive scaling parameter estimates. The simulation over the cardiac cycle is performed starting from the unloaded reference (black arrows).

Article Snippet: The computational cardiac biomechanics model was adapted from the Living Heart Human Project (version 2.1, Simulia, Dassault Systèmes) , previously used for biomechanical simulation studies by ( ; , ; ; , ; ; ; ; , ; ; ; ; ; ; ; ).

Techniques: In Vivo