A Dynamic Data Driven Grid System for Intraoperative Image Guided Neurosurgery

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A Dynamic Data Driven Grid System for
Intra-operative Image Guided Neurosurgery

• A Majumdar1, A Birnbaum1, D Choi1, A Trivedi2,
  S. K. Warfield3, K. Baldridge1, and Petr Krysl2
• 1 San Diego Supercomputer Center 2 Structural
  Engineering Dept University of California San
  Diego
• 3 Computational Radiology Lab Brigham and
  Womens HospitalHarvard Medical School
• Grants NSF ITR 0427183, 0426558 NIHP41
  RR13218, P01 CA67165, LM0078651, I3 grant (IBM)

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Neurosurgery Challenge

• Challenges
• Remove as much tumor tissue as possible
• Minimize the removal of healthy tissue
• Avoid the disruption of critical anatomical
  structures
• Know when to stop the resection process
• Compounded by the intra-operative brain
  deformation as a result of the surgical process
• Important to quantify and correct for these
  deformations while surgery is in progress
• Real-time constraints provide images
  once/hour within few mins during surgery lasting
  6 to 8 hours

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Intraoperative MRI Scanner at BWH(0.5 T)
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Brain Deformation

Before surgery
After surgery
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Overall Process

• Before image guided neurosurgery
• During image guided neurosurgery

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Timing During Surgery

Time (min)
0
20
10
30
40
Before surgery
During surgery
Preop segmentation
Intraop MRI
Segmentation
Registration
Surface displacement
Biomechanical simulation
Visualization
Surgical progress
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Current Prototype DDDAS Inside Hospital
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Two Research Aspects

• Grid Architecture grid scheduling, on demand
  remote access to multi-teraflop machines, data
  transfer/sharing
• Development of detailed advanced non-linear
  scalable viscoelastic biomechanical model

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Intra-op MRI with pre-op fMRI
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Queue Delay Experiment on TeraGrid Clusters

• TeraGrid is a NSF funded grid infrastructure
  across multiple research and academic sites
• Queue delays at SDSC and NCSA TG were measured
  over 3 days for 5 mins wall clock time on 2 to 64
  CPUs
• Single job submitted at a time
• If job didnt start within 10 mins, job
  terminated, next one processed
• What is the likelihood of job running
• 313 jobs to NCSA TG cluster and 332 to SDSC TG
  cluster 50 to 56 jobs of each size on each
  cluster

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of submitted tasks that run as a function of
CPUs requested
TeraGrid Experiment Results

Average queue delay for tasksthat began running
within10 mins
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Data Transfer

• We are investigating grid based data transfer
  mechanisms such as globus-url-copy, SRB
• All hospitals have firewalls for security and
  patient data privacy single port of entry to
  internal machines

Transfer time in seconds for 20 MB file
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Mesh Model with Brain Segmentation
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Current and New Biomechanical Models

• Current linear elastic material model RTBM
• Advanced biomechanical model FAMULS (AMR)
• Advanced model is based on conforming adaptive
  refinement method
• Inspired by the theory of wavelets this
  refinement produces globally compatible meshes by
  construction
• Replicate the linear elastic result produced by
  RTBM using FAMULS

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FEM Mesh FAMULS RTBM

RTBM (Uniform)
FAMULS (AMR)
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Deformation Simulation After Cut

No AMR FAMULS
RTBM
3 level AMR FAMULS
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Advanced Biomechanical Model

• The current solver is based on small strain
  isotropic elastic principle
• New biomechanical model
• Inhomogeneous scalable non-linear viscoelastic
  model with AMR
• Increase resolution close to the level of MRI
  voxels i.e. millions of FEM meshes
• New high resolution complex model still has to
  meet the real time constraint of neurosurgery
• Requires fast access to remote multi-tflop systems

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Parallel Registration Performance
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Parallel Rendering Performance
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Parallel RTBM Performance
(43584 meshes, 214035 tetrahedral elements)

60.00
50.00
IBM Power3
40.00
Elapsed Time (sec)
30.00
IA64 TeraGrid
20.00
IBM Power4
10.00
-
1
2
4
8
16
32
of CPUs
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End to End (BWH ? SDSC?BWH) Timing

• RTBM not during surgery
• Rendering - during Surgery

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End-to-end Timing of RTBM

• Timing of transferring 20 MB files from BWH to
  SDSC, running simulations on 16 nodes (32 procs),
  transferring files back to BWH 9 (60 7)
  50 124 sec.
• Capable of providing biomechanical brain
  deformation simulation results (using the linear
  elastic model) to the surgery room at BWH within
  2 mins using TG machines at SDSC

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End-to-end Timing of Rendering

• Intra-op MRI data sent from BWH to SDSC during a
  surgery, parallel rendering performed at SDSC,
  rendered viz sent back to BWH (but not shown to
  surgeons)
• Total time (for two sets of data) 253 2
  7.4 0.2 13.7 148.4 sec

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Current and Future DDDAS Research

• Continuing research and development in grid
  architecture, on demand computing, data transfer
• Continuing development of advanced biomechanical
  model and parallel algorithm
• Future DDDAS - near-continuous instead of once an
  hour 3-D MRI based
• Scanner at BWH can provide one 2-D slice every 3
  sec or three orthogonal 2-D slices every 6 sec
• Near-continuous DDDAS architecture
• Requires major research, development and
  implementation work in the biomechanical
  application domain
• Requires research in the closed loop system of
  dynamic image driven continuous biomechanical
  simulation and 3-D volumetric FEM results based
  surgical navigation and steering