ABSTRACT
Advances in computational methods and hardware platforms provide efficient processing of medical-imaging datasets for surgical planning. For neurosurgical interventions employing a straight access path, planning entails selecting a path from the scalp to the target area that's of minimal risk to the patient. A proposed GPU-accelerated method enables interactive quantitative estimation of the risk for a particular path. It exploits acceleration spatial data structures and efficient implementation of algorithms on GPUs. In evaluations of its computational efficiency and scalability, it achieved interactive rates even for high-resolution meshes. A user study and feedback from neurosurgeons identified this methods' potential benefits for preoperative planning and intraoperative replanning.
