Real‐time Volumetric Deformable Models for Surgery Simulation using Finite Elements and Condensation

Bro-Nielsen, Morten; Cotin, Stéphane

doi:10.1111/1467-8659.1530057

Cited by 419 publications

(261 citation statements)

References 5 publications

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“…When modelling large complex structures the number of nodes yields a huge equation system unsuitable for interactive applications [6]. Methods using explicit inversion of the stiffness matrix in order to obtain interactive deformations have already been demonstrated [1].…”

Section: Static Finite Element Methodsmentioning

confidence: 99%

“…If cutting should be performed the traditional surface based models can not be used, since these models are empty inside and a cut will not open up for any underlying tissue [1]. Cutting requires volumetric models of the tissue.…”

Section: Introductionmentioning

confidence: 99%

“…The Finite Element Method (FEM) which produces local mesh-based models has been used for different types of surgery simulation, i.e. craniofacial surgery [10], the human leg [1], the liver [4], and the gal bladder [11].…”

Section: Introductionmentioning

confidence: 99%

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Using region-of-interest based finite element modelling for brain-surgery simulation

Hansen

Larsen

1998

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

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Abstract. Brain surgery simulation requires a mathematical model of the geometric and elastic properties of the entire brain. To allow for realtime manipulation of the model it is necessary to differentiate the level of accuracy between different subparts of the brain model. A Finite Element Model (FEM) of the brain is presented capable of differentiating the spatial and temporal accuracy in different parts of the model. In a user defined region-of-interest around the surgical target point a dynamic FEM model is used to give high accuracy. The remaining parts of the brain is modelled by a static FEM model having less accuracy. The two models are integrated into one model for the entire brain using Condensation. In the context of our early version of a brain surgery simulator we have tested the condensed model versus a full dynamic model of the brain. Promising results concerning spatial error and execution time are shown.

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Section: Static Finite Element Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using region-of-interest based finite element modelling for brain-surgery simulation

Hansen

Larsen

1998

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

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“…Virtual reality deformable models. Real time simulations of linear elastic properties are based on mechanical models like the massspring models [117][118][119][120][121][122][123]. Owing to their limitations (like topological design, validity of deformations, dynamic behavior, visualization) of modeling only linear elastic properties, these models are used only to realistically animate tissue deformations, not to simulate the exact physical behavior of human soft tissue [124].…”

Section: Classification Of Hard and Soft Tissuementioning

confidence: 99%

Recent development on computer aided tissue engineering — a review

Sun¹,

Lal²

2002

Computer Methods and Programs in Biomedicine

331

164

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The utilization of computer-aided technologies in tissue engineering has evolved in the development of a new field of computer-aided tissue engineering (CATE). This article reviews recent development and application of enabling computer technology, imaging technology, computer-aided design and computer-aided manufacturing (CAD and CAM), and rapid prototyping (RP) technology in tissue engineering, particularly, in computer-aided tissue anatomical modeling, three-dimensional (3-D) anatomy visualization and 3-D reconstruction, CAD-based anatomical modeling, computer-aided tissue classification, computer-aided tissue implantation and prototype modeling assisted surgical planning and reconstruction.

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“…The increased computational demands of finite elements are serious hurdles for real-time simulation. Numerical techniques, including pre-computation of key deformations, are proposed in [6,7] to significantly reduce computation. The endoscopic training tool of [8] describes a system which uses either mass-spring or FEM depending on the situation.…”

Section: Related Workmentioning

confidence: 99%

A Microsurgery Simulation System

Brown

Montgomery

Latombe

et al. 2001

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001

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Abstract. Computer systems for surgical planning and training are poised to greatly impact the traditional versions of these tasks. These systems provide an opportunity to learn surgical techniques with lower costs and lower risks. We have developed a virtual environment for the graphical visualization of complex surgical objects and real-time interaction with these objects using real surgical tools. An application for microsurgical training, in which the user sutures together virtual blood vessels, has been developed. This application demonstrates many facets of our system, including deformable object simulation, tool interactions, collision detection, and suture simulation. Here we present a broad outline of the system, which can be generalized for any anastomosis or other procedures, and a detailed look at the components of the microsurgery simulation.

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Real‐time Volumetric Deformable Models for Surgery Simulation using Finite Elements and Condensation

Cited by 419 publications

References 5 publications

Using region-of-interest based finite element modelling for brain-surgery simulation

Using region-of-interest based finite element modelling for brain-surgery simulation

Recent development on computer aided tissue engineering — a review

A Microsurgery Simulation System

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