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