Name
Abstract | ||
|---|---|---|
Haptic and virtual reality-based surgery simulators are starting to be utilized to train surgical residents for some simple procedures, allowing them to operate on virtual human models with the aid of haptic devices with force feedback, overcoming training constraints and limitations such as a shortage of specimens, space, time and usage frequency. Compared with conventional training methods, surgery simulators have many advantages such as being risk-free and reusable, and training sessions can be stored and reviewed by physicians. However, it is very difficult to establish an accurate and efficient model for soft tissue deformation and cutting because human tissue is a special elastomeric material with non-linear, viscoelastic, anisotropic and incompressible properties. The cutting operation can change or destroy the topology of the initial model, making the entire modelling process very challenging. In this paper, four existing soft tissue cutting modelling methods are reviewed in detail -a mesh-based finite element method, a meshless method, a hybrid mesh-based and meshless method (HMMM) and an extended finite element method (XFEM). The advantages and disadvantages of each of these four algorithms are then compared and analysed in terms of a number of criteria, including their calculation speed, simulation precision, convergence and stability. Some suggestions are given for the XFEM and HMMM, which are now hot and active research topics in this field. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.2316/Journal.206.2017.3.206-4754 | INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION |
Keywords | Field | DocType |
Biomedicine, bio-robotics, soft tissue, cutting model, virtual surgery | Mechanical engineering,Manufacturing engineering,Control engineering,Biorobotics,Biomedicine,Engineering,Soft tissue | Journal |
Volume | Issue | ISSN |
32 | 3 | 0826-8185 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiangqiang Cheng | 1 | 1 | 1.36 |
| Peter Xiaoping Liu | 2 | 1054 | 49.86 |
| Pinghua Lai | 3 | 1 | 0.69 |
| Shaoping Xu | 4 | 14 | 2.57 |
| Yanni Zou | 5 | 6 | 1.10 |
| Chunquan Li | 6 | 95 | 12.61 |
| Lingyan Hu | 7 | 42 | 7.96 |