University of California, San Diego San Diego Supercomputer Center Computational Radiology Laboratory Brigham & Women’s Hospital, Harvard Medical School

University of California, San Diego

San Diego Supercomputer CenterComputational Radiology LaboratoryBrigham & Women’s Hospital, Harvard Medical School

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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 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)



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TALK SECTIONS

1. PROBLEM DESCRIPTION AND DDDAS2. GRID ARCHITECTURE3. ADVANCED BIOMECHANICAL MODEL4. PARALLEL AND END-to-END TIMING5. SUMMARY



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1. PROBLEM DESCRIPTION AND DDDAS



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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 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



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Intraoperative MRI Scanner at BWH



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Brain Shape Deformation

Before surgeryBefore surgery After surgeryAfter surgery



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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



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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



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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



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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



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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



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Example of visualization: Intra-op Brain Tumor with Pre-op fMRI



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2. GRID ARCHITECTURE



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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



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% of submitted tasks that run, as a fn of CPUs requested



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Average queue delay for tasks that began running within10 mins



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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



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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 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



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3. ADVANCED BIOMECHANICAL MODEL



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Mesh Model with Brain Segmentation



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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



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FEM Mesh : FAMULS & RTBM

RTBM (Uniform)RTBM (Uniform)FAMULS (AMR)FAMULS (AMR)



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Deformation Simulation After Cut

No – AMR FAMULSNo – AMR FAMULS 3 level AMR FAMULS3 level AMR FAMULS RTBM RTBM



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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



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4. PARALLEL AND END-to-END TIMING



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Parallel Registration Performance

0

500

1000

1500

2000

2500

3000

1 2 3 4

# of CPUs

Ela

pse

d T

ime

(sec

)

patient1

patient2



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Parallel Rendering Performance



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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



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End to End (BWH SDSCBWH) 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.

• 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



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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



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5. SUMMARY



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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

Documents

University of California, San Diego San Diego Supercomputer Center Computational Radiology Laboratory Brigham & Women’s Hospital, Harvard Medical School