Accuracy of Non-linear FE Modelling for Surgical Simulation: Study Using Soft Tissue Phantom

2010 ◽  
pp. 29-41 ◽  
Author(s):  
Jiajie Ma ◽  
Adam Wittek ◽  
Surya Singh ◽  
Grand Roman Joldes ◽  
Toshikatsu Washio ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Simulation Study ◽  
Surgical Simulation ◽  
Fe Modelling ◽  
Tissue Phantom ◽  
Non Linear

scholarly journals Digital image correlation and finite element modelling as a method to determine mechanical properties of human soft tissue in vivo

2016 ◽  
Author(s):  
Kevin Mattheus Moerman
Keyword(s):  
Mechanical Properties ◽  
Soft Tissue ◽  
Bulk Material ◽  
Constitutive Models ◽  
Image Correlation ◽  
Fe Model ◽  
Fe Modelling ◽  
Tissue Phantom ◽  
Human Soft Tissue

The mechanical properties of human soft tissue are crucial for impactbiomechanics, rehabilitation engineering and surgical simulation.Validation of these constitutive models using human data remainschallenging and often requires the use of non-invasive imaging and inversefinite element (FE) analysis. Post processing data from imaging methodssuch as tagged magnetic resonance imaging (MRI) can be challenging. DigitalImage Correlation (DIC) however is a relatively straightforward imagingmethod and thus the goal of this study was to assess the use of DIC incombination with FE modelling to determine the bulk material properties ofhuman soft tissue. Indentation experiments were performed on a silicone gelsoft tissue phantom. A two camera DIC setup was then used to record the 3Dsurface deformation. The experiment was then simulated using a FE model.

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

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.

Non-Linear Control of a Neutralisation Process

1971 ◽  
Vol 4 (9) ◽  
pp. T151-T157 ◽  
Author(s):  
P D Roberts
Keyword(s):  
Simulation Study ◽  
Digital Simulation ◽  
Linear Control ◽  
Attractive Alternative ◽  
Error Signal ◽  
Linear Controller ◽  
Linear Characteristic ◽  
Non Linear ◽  
On Line ◽  
Extra Parameter

The paper describes a digital simulation study of the application of a non-linear controller to the regulation of a single stage neutralisation process. In the controller, the proportional gain increases with amplitude of controller error signal. The performance of the non-linear controller is compared with that of a conventional linear controller and with the performance obtained by employing a linear controller with a linearisation network designed to compensate for the non-linear characteristic of the neutralisation curve. Although the performance of the non-linear controller is inferior to that obtained by employing a perfect linearisation network, its performance is still considerably superior to that obtained by using a conventional linear controller when operating at a symmetrical point on the neutralisation curve. In contrast to the linearisation network technique, the non-linear controller contains only one extra parameter and can be readily tuned on-line without prior knowledge of the neutralisation curve. Hence, it can be considered as an attractive alternative for the control of neutralisation processes.

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