Dynamic Propagation Area to Simulate Soft Tissue Deformations Using Mass Spring Method

2021 ◽  
pp. 15-27
Author(s):  
Mohd Nadzeri Omar ◽  
Muhammad Hilmi Jalil
Keyword(s):  
Soft Tissue ◽  
Dynamic Propagation ◽  
Spring Method ◽  
Mass Spring ◽  
Tissue Deformations

scholarly journals Flexible Mass Spring Method for Modelling Soft Tissue Deformation

2021 ◽  
Vol 7 (2) ◽  
pp. 24-41
Author(s):  
Mohd Nadzeri Omar ◽  
Yongmin Zhong
Keyword(s):  
Soft Tissue ◽  
Soft Tissues ◽  
Tissue Deformation ◽  
Soft Tissue Deformation ◽  
Proposed Model ◽  
Spring Method ◽  
Linear And Nonlinear ◽  
Mass Spring ◽  
Tissue Deformations ◽  
Nonlinear Deformations

It is well accepted that soft tissue deformation is a combination of linear and nonlinear response. During small displacements, soft tissues deform linearly while during large displacements, soft tissues show nonlinear deformation. This paper presents a new approach for modelling of soft tissue deformation, from the standpoint of Mass Spring Method (MSM). The proposed MSM model is developed using conical spring methodology which allow the MSM model to have different stiffnesses at different displacements during deformation. The stiffness variation creates flexibility in the model to simulate any linear and nonlinear deformations. Experimental results demonstrate that the deformations by the proposed method are in good agreement with those real and phantom soft tissue deformations. Isotropic and anisotropic deformations can be accommodated by the proposed methodology via conical spring geometry and configuration of the springs. The proposed model also able to simulate typical viscoelastic behaviour of soft tissue.

Modeling of Soft Tissue Deformation Using Mass Spring Method with Nonlinear Volume Force

2021 ◽  
pp. 75-90
Author(s):  
Mohd Nadzeri Omar ◽  
Nasrul Hadi Johari ◽  
Mohd Hasnun Arif Hassan ◽  
Mohd Amzar Azizan
Keyword(s):  
Soft Tissue ◽  
Tissue Deformation ◽  
Soft Tissue Deformation ◽  
Volume Force ◽  
Spring Method ◽  
Mass Spring

scholarly journals Effect of time complexities and variation of mass spring model parameters on surgical simulation

2014 ◽  
Vol 9 (4) ◽  
Author(s):  
Salina Sulaiman ◽  
Tan Sing Yee ◽  
Abdullah Bade
Keyword(s):  
Soft Tissue ◽  
Human Liver ◽  
Soft Tissues ◽  
Surgical Simulation ◽  
Model Parameters ◽  
Spring Model ◽  
Surgical Simulator ◽  
Tissue Model ◽  
Mass Spring Model ◽  
Mass Spring

Physically based models assimilate organ-specific material properties, thus they are suitable in developing a surgical simulation. This study uses mass spring model (MSM) to represent the human liver because MSM is a discrete model that is potentially more realistic than the finite element model (FEM). For a high-end computer aided medical technology such as the surgical simulator, the most important issues are to fulfil the basic requirement of a surgical simulator. Novice and experienced surgeons use surgical simulator for surgery training and planning. Therefore, surgical simulation must provide a realistic and fast responding virtual environment. This study focuses on fulfilling the time complexity and realistic of the surgical simulator. In order to have a fast responding simulation, the choice of numerical integration method is crucial. This study shows that MATLAB ode45 is the fastest method compared to 2nd ordered Euler, MATLAB ode113, MATLAB ode23s and MATLAB ode23t. However, the major issue is human liver consists of soft tissues. In modelling a soft tissue model, we need to understand the mechanical response of soft tissues to surgical manipulation. Any interaction between haptic device and the liver model may causes large deformation and topology change in the soft tissue model. Thus, this study investigates and presents the effect of varying mass, damping, stiffness coefficient on the nonlinear liver mass spring model. MATLAB performs and shows simulation results for each of the experiment. Additionally, the observed optimal dataset of liver behaviour is applied in SOFA (Simulation Open Framework Architecture) to visualize the major effect.

A Numerical Method to Enhance the Accuracy of Mass-Spring Systems for Modeling Soft Tissue Deformations

2009 ◽  
Vol 25 (3) ◽  
pp. 271-278 ◽  
Author(s):  
Hadi Mohammadi
Keyword(s):  
Regular Function ◽  
Technical Note ◽  
Smooth Solutions ◽  
Governing Equations ◽  
Residual Function ◽  
Spring System ◽  
Mass Spring ◽  
Tissue Deformations ◽  
Spring Constants ◽  
Element Method

This technical note presents a numerical corrective technique that allows control of nonlinearity in a mass-spring system (MSS) independent of its spring constants or system topology. The governing equations of MSS in the form of ordinary differential equations or a regular function accompanied by any boundary or initial condition as known constraints, are employed to modify the results. A least-squares algorithm coupled with the finite difference method is used to discretize the basic residual function implemented in this corrective technique. This numerical solution is applicable to both static and dynamic MSS. This technique is easy to implement and has accuracy similar to that of the equivalent finite element method (FEM) solution to the same system whereas solutions are obtained in a fraction of the CPU time. The proposed technique can also be used to smooth solutions from other methods such as FEM or boundary element method (BEM).

A FAST COMPUTATION METHOD FOR SOFT TISSUE DEFORMATIONS SIMULATION BASED ON SELF-SELECTION ALGORITHM FOR EMBEDDED OPERATING AREA

2017 ◽  
Vol 17 (07) ◽  
pp. 1740016
Author(s):  
MONAN WANG ◽  
ZHIYONG MAO ◽  
XIANJUN AN
Keyword(s):  
Soft Tissue ◽  
Soft Tissues ◽  
Nonlinear Problems ◽  
Rectus Femoris ◽  
Computation Method ◽  
Biomechanical Models ◽  
Simulation Based ◽  
Material Nonlinear ◽  
Tissue Deformations ◽  
Tissue Models

This study used biomechanical models of soft tissues based on combined exponential and polynomial models. Finite element methods were used to solve material nonlinear and geometrically nonlinear problems of soft tissue models. This involved assigning a screening coefficient in the model-accelerated computing process to filter the units involved in the calculation. The screening coefficient controlled both the accuracy of the results of simulation and the computing speed through setting up a subset of finite elements. The fast computer method based on the screening coefficient was applied to the rectus femoris simulation.

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