high performance computing on graphics processing units: hgpu.org

Advances in computational methods and hardware platforms provide efficient processing of medical imaging data sets for surgical planning. In the case of neurosurgical interventions that are performed via a straight access path, planning entails selecting a pathway, from the scalp surface to the targeted area, that is of minimal risk to the patient. We propose a GPU-accelerated approach to enable quantitative estimation of the risk associated with a particular access path at interactive rates. It heavily exploits spatially accelerated data structures and efficient implementation of algorithms on GPUs. We evaluate the computational efficiency and scalability of the proposed approach through extensive performance comparisons, and show that interactive rates can be achieved even for high-resolution meshes. Through a user study, and feedback obtained from domain experts, we identify some of the potential benefits that our high-speed approach offers for pre-operative planning and intra-operative replanning of straight access neurosurgical interventions.