Clinical demands of image-guided procedures present technical challenges in X-ray 1Kx1K fluoroscopy and cone-beam CT on a mobile C-arm. Performance-per-watt and performance-per-dollar are other major considerations in a search for an optimal computational platform. Real-time constraints of processing high-resolution fluoroscopic images currently necessitate the use of highly specialized proprietary image processing hardware, which cannot be easily repurposed for acceleration of other computing tasks. In our previous studies, we were investigating heterogeneous computing architectures and suitable hardware / software components to assist in time-critical surgical applications. Through those studies, it has been shown that Graphics Processing Units (GPUs) can provide outstanding levels of computational power utilizing the Single Instruction Multiple Data (SIMD) programming model. In the present study, we expand our research in the domain of real-time processing and continue to explore the feasibility of GPU acceleration for both fluoroscopic and tomographic imaging. Current emphasis is being placed on applicability of NVIDIA's novel Tesla computing solutions and Compute Unified Device Architecture (CUDA). The results of this pilot project comprise the Cg/OpenGL and CUDA algorithm implementations, benchmark evaluations, and examples of processing image data acquired with use of anthropomorphic phantoms.
