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GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions

GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions. Mario Rincón-Nigro. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions. Straight Access Procedures. Biopsies, Deep Brain Stimulation, etc. Neurosurgeon needs to minimize risk

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GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions

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  1. GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions Mario Rincón-Nigro

  2. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions Straight Access Procedures • Biopsies, Deep Brain Stimulation, etc • Neurosurgeon needs to minimize risk • Vital structures cannot be punctured • Shorter pathways are preferable • Farther pathways are preferable • …

  3. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions Risk Maps • Brunneberg et al MICCAI 2007; Essert et al MIAR 2010; Shamir et al MICCAI 10; Navkar et al IPCAI 2010 Penalize Long Pathways Penalize Closeness to Vital Structures k1 = 0.1, k2 = 0.9 k1 = 0.5, k2 = 0.5 k1 = 0.9, k2 = 0.1

  4. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions Vital Structures • Represented as triangle meshes [Navkaret al. IPCAI 10] • 178k Triangle mesh -> Wait 3 hours for risk map (brute force) • First step towards interactive rates: • Embed geometric primitives in BVHs • 178k Triangle mesh -> Wait less than 6 seconds for risk map

  5. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions GPU-Acceleration (Improvement 1) • Second step towards interactive rates • Compute risk maps on the GPU • Set BVH layout to take advantage of GPU texture memory for caching -> Two orders of magnitude speed-up! -> GPU scales better than CPU to problem size

  6. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions GPU-Acceleration (Improvement 2) • Maximize use of GPU cores • Persistent threads + centralized task queue -> 1.4x Speed-up (~30% Time reduction). -> Application has become memory bound.

  7. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions More Performance Comparisons • Comparison to voxel-based formulation [Shamir et al, MICCAI 2010]: • Mesh-based formulation (our stuff) is both faster and scales better to problem size than voxel-based formulation

  8. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions What can We do with this Speed? • Intra-operative (re)-planning Target repositioning Controlling the speed of the needle We could also do automatic selection of paths, but neurosurgeons cannot be taken out of the loop

  9. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions Experiment on Guided and UnguidedTarget Repositioning • Subjects were asked to plan the insertion of a needle • Two treatments: 1) Visually Guided Target Positioning. 2) Risk Map Guided Target Positioning • Target-repositioning and risk map guidance resulted in the planning of safer paths: length was improved in all cases, proximity was improved for set of weights w1 • It’s difficult for people to position the target without guidance. • No Guidance = Paths far from optimal W1 = (k1 = 0.1, k2 = 0.9) ; W2 = (k1 = 0.5, k2 = 0.5) ; W3 = (k1 = 0.9, k2 = 0.1)

  10. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions Conclusions • We have solved the “computational performance” aspect of the problem Future work • Imaging acquisition and planning tool integration • Include functional areas and other constraints to the risk model • We believe this can be used for planning gamma knife interventions

  11. GPU-Accelerated Visualization and Planning of Neurosurgical Interventions Questions • Submitted to IEEE Computer Graphics and Applications • Recommended major revision • Major revision done, 2d round review

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