A new deformation simulation algorithm for elastic-plastic objects based on splat primitives

Modeling fibrous soft tissue dissection with elastic-plastic deformation for simulation of brain tumor removal

BACKGROUND AND OBJECTIVE

Realistic modeling the dissection of brain tissue is of key importance for simulation of brain tumor removal in virtual neurosurgery systems. However, existing methods are unable to characterize inelastic behaviors of brain tissue, such as plastic deformation and dissection evolution, making it ineffective in simulating brain tumor removal procedures.

METHODS

In this paper, a model of fibrous soft tissue dissection for the simulation of brain tumor removal is proposed. A dissection variable of representative volume element is used to characterize the dissection state of the fibrous soft tissue. The evolution of dissection with elastic-plastic deformation under the effects of external loads is presented.

RESULTS

Simulation results show that the proposed model provides realistic, stable and intuitive results in the simulation of fracture in fibrous soft tissues. As the external load increases, the fibrous soft tissue begins to crack, with the cracks growing and multiplying until they eventually merge to form a fracture. The proposed model is incorporated into the simulation of brain tumor removal.

CONCLUSIONS

The experimental results demonstrate the feasibility of modeling fibrous soft tissue dissection with elastic-plastic deformation. A relative high degree of realistic visual feedback is achieved.

A morphing-Based 3D point cloud reconstruction framework for medical image processing

BACKGROUND AND OBJECTIVE

In the virtual surgery simulation system, the reconstruction of a highly precise soft tissue 3D model is an effective method to improve the user's visual telepresence. However, the traditional point cloud generation method based on subdivision and filling is unsatisfactory due to its low accuracy and slow speed.

METHODS

To address this problem, we present a novel 3D point cloud reconstructing model based on Morphing. The 3D surface model of soft tissue (live) is obtained from a series of 2D CT images using Mimics. The 3D voxel model of soft tissue is reconstructed through a sequential change of the 3D surface model by utilizing Morphing. A nonlinear interpolation method is used to fit the irregular shape of the model and improve simulation accuracy.

RESULTS

The point cloud model builds from discrete points, avoiding the problems of instability and computational complexity, which are inherent in both the surface and volume models for soft tissue. Compared with the volumetric subdividing and voxel filling method, the simulation results show that the 3D cloud model reconstructed based on Morphing is more fast, accurate and consistent with the real soft tissue.

CONCLUSIONS

The simulating experiment of soft tissue deformation using 3D point cloud model which reconstructed using moprhing proved our method is effective and correct.

A high-resolution model for soft tissue deformation based on point primitives

BACKGROUND AND OBJECTIVES

In order to achieve a high degree of visual realism in surgery simulation, we propose a new model, which is based on point primitives and continuous elastic mechanics theory, for soft tissue deformation, tearing and/or cutting.

METHODS

The model can be described as a two-step local high-resolution strategy. First, appropriate volumetric data are sampled and assigned with proper physical properties. Second, sparsely sampled points in non-deformed regions and densely-sampled points in the deformed zone are selected and evaluated. By using a meshless deformation model based on point primitives for all volumetric data, the affine transform matrix of collision points can be computed. The new positions of neighboring points in the collided surface can be then calculated, and more details in the local deformed zone can be obtained for rendering. Technical details about the derivations of the proposed model as well as its implementation are given.

RESULTS

The visual effects and computation cost of the proposed model are evaluated and compared with conventional primitives-based methods. Experimental results show that the proposed model provides users (trainees) with improved visual feedback while the computational cost is at the same magnitude of other similar methods.

CONCLUSIONS

The proposed method is especially suitable for the simulation of soft tissue deformation and tearing because no grid information needs to be maintained. It can simulate soft tissue deformation in a high degree of authenticity with real-time performance. It could be considered implemented in the development of a mixed reality application of neurosurgery simulators in the future.