9338

Simulation of Biological Tissue using Mass-Spring-Damper Models

Emil Eriksson
School of Science and Technology, Orebro University, Sweden
Orebro University, 2013
@phdthesis{eriksson2013simulation,

   title={Simulation of Biological Tissue using Mass-Spring-Damper Models},

   author={Eriksson, Emil},

   year={2013},

   school={"O}rebro University}

}

Download Download (PDF)   View View   Source Source   

415

views

The goal of this project was to evaluate the viability of a mass-spring-damper based model for modeling of biological tissue. A method for automatically generating such a model from data taken from 3D medical imaging equipment including both the generation of point masses and an algorithm for generating the spring-damper links between these points is presented. Furthermore, an implementation of a simulation of this model running in real-time by utilizing the parallel computational power of modern GPU hardware through OpenCL is described. This implementation uses the fourth order Runge-Kutta method to improve stability over similar implementations. The difficulty of maintaining stability while still providing rigidness to the simulated tissue is thoroughly discussed. Several observations on the influence of the structure of the model on the consistency of the simulated tissue are also presented. This implementation also includes two manipulation tools, a move tool and a cut tool for interaction with the simulation. From the results, it is clear that the mass-springdamper model is a viable model that is possible to simulate in real-time on modern but commoditized hardware. With further development, this can be of great benefit to areas such as medical visualization and surgical simulation.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

149 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1241 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: