Surface rendering examples

Sample images. Surface rendering of CT image data

Just a few more sample images created with a wxPython OpenGL rendering application...

Medical imaging

Image of wxpython OpenGL application rendering 2959572 vertices and 986524 triangles real time.

OpenGL is used to process and visualize medical imaging data. The image shows a wxpython application that I wrote do display the triangulated surface structure of meshed CT imaging data. The surface data consist of  2959572 vertices and 986524 triangles. With the use of OpenGL manipulation (pan, zoom, rotate) can be performed real time. For shading I used OpenGL Shading Language (GLSL) which allows you to easily apply lighting effects to your view.

The GLSL shaders can be downloaded here:  https://www.dropbox.com/sh/ip306uavm7jpekg/6FENd-2TUj/GLSL/

Links:
OpenGL - http://www.opengl.org/
GLSL - http://www.opengl.org/documentation/glsl/
wxpython - http://wxpython.org/

Patient-specific finite element simulation of radiofrequency ablation

Tetrahedral FE model for patient-specific simulation of radiofrequency ablation in the liver. The model geometry was generated based on patent-specific CT-image data. The image shows liver vasculature tumor, ablation probe, and surrounding bone structure.

Lets get this blog started with one of my favorite images of an python application (http://www.youtube.com/watch?v=jTXbjQ_6RWM) that I created to generate finite element (FE) models for simulation of radiofrequency ablation (RFA). The FE method is an established technique to solve the thermal-electrical differential equations for realistic RFA treatment simulation. Nevertheless, generation of patient-specific FE models is a labor intense task and requires use of multiple software tools. In order to overcome the challenges of modeling I generated a research tool as well as a workflow to generate patient-specific FE models for simulation of RF ablation efficiently. Common techniques of image processing and image meshing are combined with new approaches, such as interactive electrode placement, interfaces to commercial FE software programs including visualization of model results overlaid on imaging data. Such a tool may provide additional information for the treating physician for more effective pre-treatment planning of procedures. Via this tool, I was able to simulate RF ablation based on patient-specific geometry and visualize model results overlaid on CT imaging data. 

To create the application I used:

• Python (http://www.python.org)
• Numpy (http://numpy.scipy.org) 
• Tkinter (http://wiki.python.org/moin/TkInter)
• PyOpenGL; OpenGL,GLSL (http://pyopengl.sourceforge.net)
•  f2py (http://cens.ioc.ee/projects/f2py2e)