Alumnus of the Pattern Recognition Lab of the Friedrich-Alexander-Universität Erlangen-Nürnberg
Just be faster
Research interests
<font size="+1"><a href="http://www5.informatik.uni-erlangen.de/research/projects/hardware-acceleration-techniques-for-3d-cone-beam-reconstruction/">Hardware Acceleration Techniques for 3D Cone-Beam Reconstruction</a></font>
<font size="+1">High Performance Medical Image Processing</font>
<font size="+1">High Performance Iterative Reconstruction using Graphics Processings Units and CUDA</font>
The video on the right shows an on-the-fly (FBP) reconstruction and visualization of a head acquired by a C-arm CT system
The reconstruction and visualization was performed on first generation CUDA-capable graphics cards by NVIDIA
a GeForce 8800 GTX with 768MB memory. The reconstructed volume is a 5123-cube using floats (512MB). For the reconstruction 414 projections, each a size of 10242 floating-point pixels,
were streamed onto the graphics card and than back-projected on the volume. Details about the implementation and reconstruction speed can be found in our
paper. Note that the direkt visualization of a slices during reconstruction reduces the computational speed due to
extra costs. After all projections are back-projected we slide through the volume to show that not only one slice was reconstructed.