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GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions
Jan.-Feb. 2014 (vol. 34 no. 1)
pp. 22-31
Mario Rincon-Nigro, Univ. of Houston, Houston, TX, USA
Nikhil V. Navkar, Univ. of Houston, Houston, TX, USA
Nikolaos V. Tsekos, Univ. of Houston, Houston, TX, USA
Zhigang Deng, Univ. of Houston, Houston, TX, USA
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.
Index Terms:
Medical image processing,Neurosurgery,Graphics processing units,Instruction sets,Data structures,interactive visualizations,GPU acceleration,neurosurgical interventions,risk maps,straight access,computer graphics,visualizations
Mario Rincon-Nigro, Nikhil V. Navkar, Nikolaos V. Tsekos, Zhigang Deng, "GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions," IEEE Computer Graphics and Applications, vol. 34, no. 1, pp. 22-31, Jan.-Feb. 2014, doi:10.1109/MCG.2013.35
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