A new optimization approach for mass-spring models parameterization

Silva, Josildo Pereira da; Giraldi, Gilson A.; Apolinário, Antônio L.

doi:10.1016/j.gmod.2015.07.001

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Recently, a new approach has been proposed to resolve the first problem. 20 This method just compares the results with some physical models that simulate homogeneous, linear, and isotropic material with isoparametric finite element method (FEM) techniques. As far as we know, the simulation error is still too large to meet the requirements of brain simulation accuracy.…”

Section: Deformation Methodsmentioning

confidence: 99%

“…However, this method has the following disadvantages: (a) that mass–spring parameters and material properties are not relevant, and (b) the instability and inaccuracy problems such as node flipping. Recently, a new approach has been proposed to resolve the first problem . This method just compares the results with some physical models that simulate homogeneous, linear, and isotropic material with isoparametric finite element method (FEM) techniques.…”

Section: Related Workmentioning

confidence: 99%

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A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

Tian

Shen

2018

Computer Animation & Virtual

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This paper offers a fast multi‐graphics processing unit (GPU) parallel simulation framework to the problem of real‐time and nonlinear finite element computation of brain deformation. A load balancing strategy is proposed to ensure the efficient distribution of nonlinear finite element computation on multi‐GPU. A data storage structure is designed to minimize the amount of data transfer and make full use of the overlay technique of GPU to reduce the transferring latency between multi‐GPUs. We further present a fast central processing unit (CPU)–GPU parallel continuous collision detection and response method, which not only can deal with the collision between the brain and skull but also can handle the self‐collision of the brain. Our method can make full use of CPU and GPU to implement a parallel computation about deformation and collision detection. Our experimental results show that our method is able to handle a brain geometric model with high detail gyrus composed of more than 40,000 tetrahedron elements. This can facilitate the fidelity of the current virtual brain surgery simulator. We evaluate our approach qualitatively and quantitatively and compare it with related works.

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Section: Deformation Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

Tian

Shen

2018

Computer Animation & Virtual

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“…The advantage of the physical model is that it considers the inherent parameters of the fabric and has a strong sense of reality in the simulation (Celniker and Gossard, 1991;Breen et al, 1994;Provot, 1995;Eischen et al, 1996;Howlett and Hewitt, 1998). However, the mechanical model needs to analyze the force and establish the equation of motion point by point, involving a large number of partial differential equations, which makes the simulation process very time-consuming (Terzopoulos et al, 1987;Okabe et al, 1992;Jakobsen, 2001;Da Silva et al, 2015). The energy model must iterate multiple times to find the minimum energy that makes the physical model less real-time (Ng and Grimsdale, 1996;Eberhardt et al, 1996;Peng and Zhang, 2005).…”

Section: Introductionmentioning

confidence: 99%

Fabric modeling based on Riemann zero curvature

Tao

et al. 2022

IJCST

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PurposeFabric has complicated anisotropic mechanical behavior because of the woven pattern and complex physical properties. However, most current fabric simulation models are not satisfied because the models are usually geometrical models with stiffness parameters.Design/methodology/approachIn this paper, the authors present a modeling technique to simulate fabric with Riemann manifold. The proposed nonlinear model is formed with ridge wave-curved surface based on the Riemann zero curvature, and the authors develop a solution to conserve the surface area. It decomposes the m × n matrix constituting the fabric into several batches and processes the fabric dots in batches. In this model, the distance between any two adjacent particles of the fabric's is assumed to be equal, and the area of the curved surface is always constant, and the inclination and decay of the ridge wave-curved surface are also considered.FindingsAs the result, the simulated shape is lifelike. In time cost performance, the model improves the efficiency of the fabric styling and meets the requirements of real-time simulation.Originality/valueThe proposed nonlinear model is formed with ridge wave-curved surface based on the Riemann zero curvature, and the authors develop a solution to conserve the surface area.

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“…Hou et al proposed a new constraint-based finite element model for brain tissue that was implemented by introducing implicit integration and Graphics Processing Unit calculation [8]. Pereira da Silva et al proposed a new method based on data-driven parameterization of the mass-spring model to achieve object deformation [9], [10]. Tang and Wan proposed a new strain-based constrained finite element method for simulating effectively nonlinear uniform soft structures [11].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Component Conical Spring Model of Soft Tissue in Virtual Surgery

Dong

Yang

et al. 2020

IEEE Access

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With the rapid development of optical system and medical robotic technology, the virtual surgery approach has achieved good progress but also faces many challenges. Physical and mechanical models of soft tissue characteristics have been a hot research topic in this field. In the present investigation, a physical model with a new multi-component conical spring is proposed, which replaces the cylindrical spring of the traditional mass-spring model. In the new model, a multi-component spring structure has been incorporated that consists of a conical spring and a linear spring in series. Based on the analysis of the nonlinear characteristics of soft tissue, the mechanical model was established to correspond better with the physical behavior. To determine the parameters for the mechanical model, the Euler method was deployed. The determination of the model parameters was achieved using physical tests. Result simulation experiments confirmed that the behavior of the new multi-component conical spring model proposed in the present paper gives results that are closer to those obtained from real-world physical tests than were data given by either the mass-conical-spring model or the mass-spring-model. Average difference, maximum difference and mean square error values were used for the evaluation. The accuracy of simulations for soft tissue virtual surgery can be improved by application of the new model developed in the present study. INDEX TERMSVirtual reality, computer vision, medical robotics, soft tissue modeling, conical spring, mechanical model, multi-component conical spring model.

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A new optimization approach for mass-spring models parameterization

Cited by 8 publications

References 11 publications

A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

A multi‐GPU finite element computation and hybrid collision handling process framework for brain deformation simulation

Fabric modeling based on Riemann zero curvature

A Multi-Component Conical Spring Model of Soft Tissue in Virtual Surgery

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