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GPU-Accelerated Interactive Visualization and Planning of Neurosurgical Interventions
Mario Rincón-Nigro
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– …
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
GPU-Accelerated Visualization and Planning of Neurosurgical Interventions
Penalize LongPathways
Penalize Closenessto Vital Structures
k1 = 0.1, k2 = 0.9 k1 = 0.5, k2 = 0.5 k1 = 0.9, k2 = 0.1
Vital Structures
• Represented as triangle meshes [Navkar et al. IPCAI 10]– 178k Triangle mesh -> Wait 3 hours for risk map (brute force)
GPU-Accelerated Visualization and Planning of Neurosurgical Interventions
• First step towards interactive rates:• Embed geometric
primitives in BVHs• 178k Triangle mesh ->
Wait less than 6 seconds for risk map
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
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.
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
GPU-Accelerated Visualization and Planning of Neurosurgical Interventions
What can We do with this Speed?• Intra-operative (re)-planning
GPU-Accelerated Visualization and Planning of Neurosurgical Interventions
Target repositioning Controlling the speed of the needle
We could also do automatic selection of paths,but neurosurgeons cannot be taken out of theloop
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
GPU-Accelerated Visualization and Planning of Neurosurgical Interventions
W1 = (k1 = 0.1, k2 = 0.9) ; W2 = (k1 = 0.5, k2 = 0.5) ; W3 = (k1 = 0.9, k2 = 0.1)
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
GPU-Accelerated Visualization and Planning of Neurosurgical Interventions
Questions
GPU-Accelerated Visualization and Planning of Neurosurgical Interventions
-Submitted to IEEE Computer Graphics and Applications
- Recommended major revision- Major revision done, 2d round review