Simulated vascular reconstruction in a virtual operating theatre Robert G. Belleman, Peter M.A. Sloot Section Computational Science, University of Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, the Netherlands.

Overview n Interactive distributed simulation –Simulated vascular reconstruction in a virtual environment n Performance issues –High speed simulation –High speed communication –Responsive interactive exploration n Time/location independence

Interactive distributed simulation n Human in the loop experimentation –interactive exploration of data/parameter spaces

Performance issues n Asynchronous, pipelined configuration –Distributed to exploit specialised resources –To increase performance: n decrease component execution times n shorten delays

Vascular reconstruction n “Traditional” treatment

Vascular reconstruction (Figure courtesy C.A. Taylor, Stanford University)

Simulated vascular reconstruction n Simulated treatment planning

Simulated vascular reconstruction n Components: –blood flow simulation –visualization in a virtual environment –interaction with simulation and visualization (treatment planning) n Requirements: –Interactive system; fast response, fast update rate

Fluid flow simulation n Lattice Boltzmann Method (LBM) –Lattice based particle method n Regular lattice, similar to CT or MRI datasets n Allows irregular 3D geometry n Allows changes at run-time n Velocity, pressure and shear stress calculated from particle densities n Non-compressible homogeneous fluid, laminar flow n Spatial and temporal locality –Ideal for parallel implementation

Preprocessing n Segmentation of patient specific MRA/CTA scan –Isolates region of interest n Lattice Boltzmann grid generation –Defines solid and fluid nodes, inlet and outlet conditions

Interactive exploration in VR n Visualize simulation results –Flow field, pressure, shear stress –Real time n Interactive exploration –VR interaction to locate regions of interest n Interactive grid editing –Simulate vascular reconstruction procedure

Interactive exploration in VR n Visualize simulation results –Flow field, pressure, shear stress –Real time n Interactive exploration –VR interaction to locate regions of interest n Interactive grid editing –Simulate vascular reconstruction procedure

Parallel fluid flow simulation n Performance indication:

Communication delay n Mb of data per iteration –Velocity, pressure, shear stress –May take seconds to transfer n Increasing communication throughput 1.Latency hiding n Decrease latency: faster response time, increased update rate 2.Payload reduction n Less data: shorter transfer times

Latency hiding n Multiple connections –Waiting for acknowledgements is hidden –Packets can travel through different routes –Uses CAVERN

Payload reduction n Data encoding –Decreases level of detail n Accuracy determined by type of representation –Lossy compression –Induces latency n “Standard” compression libraries (zlib) –Reduction to 10% not uncommon –Lossless compression –Induces latency

Communication pipeline n Cascade of encoding, compression and multiple socket connections

Pipeline performance Latency hiding : Payload reduction :

The Virtual Laboratory n Shared use of distributed computing resources: high performance computers, scanners, algorithms, etc. –Connected via high performance networks –Common infrastructure: the Virtual Laboratory n Multi-disciplinary scientific experimentation –Problem solving environments (PSE) –Time/location independent scientific experimentation –Collaborative scientific research For additional information... DutchGrid initiative: VLAM-G :

Experiment definition n Simulated vascular reconstruction –Patient specific angiography data –Fluid flow simulation software –Simulation of reconstructive surgical procedure in VR –Interactive visualization of simulation results in VR n Pre-operative planning –Explore multiple reconstruction procedures

Where are we going? n Improve blood flow simulation n High performance visualization and rendering n Time/location(/device?) independent interactive simulation –Access to high performance computing resources over low bandwidth connections –Use anywhere, at any time… on anything?

Where are we going? n Improve blood flow simulation n High performance visualization and rendering n Time/location(/device?) independent interactive simulation –Access to high performance computing resources over low bandwidth connections –Use anywhere, at any time… on anything?

Where are we going? n Improve blood flow simulation n High performance visualization and rendering n Time/location(/device?) independent interactive simulation –Access to high performance computing resources over low bandwidth connections –Use anywhere, at any time… on anything?

Where are we going? n Improve blood flow simulation n High performance visualization and rendering n Time/location(/device?) independent interactive simulation –Access to high performance computing resources over low bandwidth connections –Use anywhere, at any time… on anything?

Thanks! n UvA, Section Computational Science –Sloot, Hoekstra, Zhao, Artoli, Merks, Shulakov, Shamonin n Leiden University Medical Center (LUMC) –LKEB (Reiber, vd Geest, Koning, Schaap) n Stanford University –Biomedical department (Zarins, Taylor) n SARA Computing and Networking Services Related work: n Lattice Boltzmann flow simulation: Artoli, Hoekstra (UvA/SCS) n Segmentation of MRA images: Koning, Schaap (LUMC) n Agent based solutions to interactive systems: Zhao (UvA/SCS)