CUDA Spotlight: GPU-Accelerated Medical Image Processing
By Calisa Cole, posted Dec 02 2011 at 12:00PM
This week's Spotlight is on Anders Eklund, a SPh.D. student at Linköping University in Sweden. Anders is affiliated with the University's Center for Medical Image Science and Visualization (CMIV), a multidisciplinary research center.
NVIDIA: Anders, what is your research focused on?
My research is especially focused on functional magnetic resonance imaging (fMRI), where you try to find brain activity from magnetic resonance images (MRIs) of the brain. My interests include brain-computer interfaces (BCI) with real-time fMRI, where the fMRI data is processed in real-time as the subject is in the MR scanner. A brain computer interface could help people communicate who are paralyzed or suffer from Locked-In syndrome.
NVIDIA: How did you become interested in this area?
NVIDIA: How does GPU computing play a role in your work?
Another challenge in the medical imaging domain is the amount of data that is collected for a single patient. A 4D (3D + time) computed tomography (CT) dataset can be of the resolution 512 x 512 x 512 x 20 and require as much as 10 GB of storage. To apply image denoising to such a dataset can take several hours on the CPU, compared to 15-20 minutes on the GPU.
NVIDIA: What are some advantages of working with CUDA?
I like the different tools that NVIDIA has developed (like the Visual Profiler) and the fact that NVIDIA continues to improve the CUDA programming model by listening to people who use CUDA.
NVIDIA: As computing becomes more powerful, what can we look forward to?
Anders is currently financed by the Linnaeus center CADICS, funded by the Swedish research council, and the Neuroeconomic research group at Linköping University.