|IV system using CUDA Click
“We have evaluated various development environments for parallel computing,” says Herlambang, the researcher responsible for the CUDA implemention, “but we chose CUDA because it lets us do development in the C language syntax we are used to. Also, we will be able to take advantage of speed increases in future generations of GPUs without modifying the system we have developed. If an environment that makes it easy to debug large CUDA programs becomes available, CUDA will become an even more powerful development environment for parallel computing and we expect it will find more applications in medical image processing as well.”
When images from CT and MRI are viewed stereoscopically in real time, physicians can check the state of diseased tissues and make diagnoses without biopsies and surgery. Moreover, several physicians can view the images at the same time and consult with one another. And it may eventually make it possible for several physicians to perform arthroscopic surgery and other minimally invasive surgical techniques together, with each surgeon able to visualize the operation in real time.
It is difficult to bring a huge parallel computer array into clinical settings, but the powerful computing capabilities of GPUs and Tesla make it possible to provide compact, parallel computing modules.
Prof. Takeyoshi Dohi, Advanced Therapeutic & Rehabilitation Engineering Laboratory: http://www.atre.t.u-tokyo.ac.jp/en/
Associate Professor Hongen Liao:
▼Introduction to Laboratory
ATRE Lab, Dept. of Mechano-Informatics, Graduate School of Information Science and Technology,
The University of Tokyo, where Prof. Dohi and his colleagues have pioneered computer-aided surgery, is one of the world’s top laboratories in the medical engineering field.
The 3D stereoscopy technology presented here has given rise to numerous advanced devices, including microsurgery robots, wedge prism endoscopes, and surgical stereoscopic composite displays.