University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
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 University of California San Diego
2 Structural Engineering Dept University of California San Diego
3 Computational Radiology Lab Brigham and Women’s Hospital
Harvard Medical School
Grants: NSF: ITR 0427183,0426558; NIH:P41 RR13218, P01 CA67165, LM0078651, I3 grant (IBM)
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
TALK SECTIONS
1. PROBLEM DESCRIPTION AND DDDAS2. GRID ARCHITECTURE3. ADVANCED BIOMECHANICAL MODEL4. PARALLEL AND END-to-END TIMING5. SUMMARY
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
1. PROBLEM DESCRIPTION AND DDDAS
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
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 shape deformation that happens as a result of the surgical process – preoperative plan diminishes
• Important to be able to quantify and correct for these deformations while surgery is in progress by dynamically updating pre-operative images in a way that allows surgeons to react to these changing conditions
• The simulation pipeline must meet the real-time constraints of neurosurgery – provide images approx. once/hour within few minutes during surgery lasting 6 to 8 hours
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Intraoperative MRI Scanner at BWH
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Brain Shape Deformation
Before surgeryBefore surgery After surgeryAfter surgery
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Overall Process• Before image guided neurosurgery
• During image guided neurosurgery
Segmentation and Visualization
Preoperative Planning ofSurgical Trajectory
Preoperative
Data Acquisition
Preoperative data
Intraoperative MRISegmentation Registration
Surfacematching
Solve biomechanicalModel for volumetricdeformation
Visualization Surgicalprocess
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Timing During Surgery
Time (min)
Before surgery During surgery
0 10 20 30 40
Preop segmentation
Intraop MRI
Segmentation
Registration
Surface displacement
Biomechanical simulation
Visualization
Surgical progress
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Current Prototype DDDAS Inside Hospital
Pre and Intra-op 3D MRI (once/hr)Pre and Intra-op 3D MRI (once/hr)
Local Local computercomputer
at BWHat BWH
Crude linear elastic FEM Crude linear elastic FEM solutionsolution
Merge pre and intra-op vizMerge pre and intra-op viz
Intr
a-op
sur
gica
l In
tra-
op s
urgi
cal
deci
sion
and
ste
erde
cisi
on a
nd s
teer
Segmentation, Registration, Segmentation, Registration, Surface Matching for BCSurface Matching for BC
Once every hour or twoOnce every hour or twofor a 6 or 8 hour surgeryfor a 6 or 8 hour surgery
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Current Prototype DDDAS System
• Receives 3-D MRI from operating room once/hour or so• Uses displacement of known surface points as BC to
solve a crude linear elastic biomechanical FEM material model on compute system located at BWH
• This crude inaccurate model is solvable within the time constraint of few minutes once an hour on local computers at BWH
• Dynamically updates pre-op images with biomechanical volumetric simulation based intra-op images
• Time critical updates shown to surgeons for intra-op surgical navigation
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Two Research Aspects
• Grid Architecture – grid scheduling, on demand remote access to multi-teraflop machines, data transfer• Data transfer from BWH to SDSC, solution of detail
advanced biomechanical model, transfer of results back to BWH for visualization need to be performed in a few minutes
• Development of detailed advanced non-linear scalable viscoelastic biomechanical model• To capture detail intraoperative brain deformation
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Example of visualization: Intra-op Brain Tumor with Pre-op fMRI
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
2. GRID ARCHITECTURE
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Queue Delay Experiment on TeraGrid Cluster
• 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 didn’t 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
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
% of submitted tasks that run, as a fn of CPUs requested
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Average queue delay for tasks that began running within10 mins
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Queue Delay Test Conclusion
• There appears to be a direct relationship between the size of request and the length of the queue delay
• Two clusters exhibit different performance profiles
• This behavior of queue systems clearly merits further study
• More rigorous statistical characterization ongoig on much larger data sets
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
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 direction
Globus-url-copy
SRB Scp Scp –C
TG to BWH 50 49 68 31
BWH to TG 9 12 40 30
Transfer time in seconds for 20 MB fileTransfer time in seconds for 20 MB file
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
3. ADVANCED BIOMECHANICAL MODEL
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Mesh Model with Brain Segmentation
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Current and New Biomechanical Model• Current linear elastic material model – RTBM• Advanced model under development - FAMULS• Advanced model is based on conforming
adaptive refinement method – FAMULS package (AMR)
• Inspired by the theory of wavelets this refinement produces globally compatible meshes by construction
• First task is to replicate the linear elastic result produced by the RTBM code using FAMULS
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
FEM Mesh : FAMULS & RTBM
RTBM (Uniform)RTBM (Uniform)FAMULS (AMR)FAMULS (AMR)
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Deformation Simulation After Cut
No – AMR FAMULSNo – AMR FAMULS 3 level AMR FAMULS3 level AMR FAMULS RTBM RTBM
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Advanced Biomechanical Model
• The current solver is based on small strain isotropic elastic principle
• The new biomechanical model will be inhomogeneous scalable non-linear viscoelastic model with AMR
• We also want to increase resolution close to the level of MRI voxels i.e. millions of FEM meshes
• Since this complex model still has to meet the real time constraint of neurosurgery it requires fast access to remote multi-tflop systems
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
4. PARALLEL AND END-to-END TIMING
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Parallel Registration Performance
0
500
1000
1500
2000
2500
3000
1 2 3 4
# of CPUs
Ela
pse
d T
ime
(sec
)
patient1
patient2
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Parallel Rendering Performance
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Parallel RTBM Performance
(43584 meshes, 214035 tetrahedral elements)
-
10.00
20.00
30.00
40.00
50.00
60.00
1 2 4 8 16 32
# of CPUs
Ela
pse
d T
ime
(sec
)
IBM Power3
IA64 TeraGrid
IBM Power4
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
End to End (BWH SDSCBWH) Timing
• RTBM – not during surgery
• Rendering - during Surgery
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
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.
• This shows that the grid infrastructure can provide biomechanical brain deformation simulation solutions (using the linear elastic model) to surgery rooms at BWH within ~ 2 mins using TG machines
• This satisfies the tight time constraint set by the neurosurgeons
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
End-to-end Timing of Rendering
• MRI data from BWH was transferred to SDSC during a surgery
• Parallel rendering was performed at SDSC• Rendered viz was sent back to BWH (but not
shown to surgeons)• Total time (for two sets of data) in sec =
2*53 (BWH to SDSC) + 2* 7.4 (render on 32 procs) + 0.2 (overlapping viz) + 13.7 (SDSC to BWH) = 148.4 sec
DURING SURGERYDURING SURGERY
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
5. SUMMARY
University of California, San Diego
San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School
ICCS2005ICCS2005
Ongoing 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
• Moving towards near-continuous DDDAS instead of once an hour or so 3-D MRI based DDDAS
• 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