Brain Science Meets Big Science
Grid Computing for Clinical Neuroscience Research
Steve Pieper, PhD
Isomics, Inc.
In collaboration with:
Surgical Planning Laboratory
An NCRR National Resource Center
Brigham and Women's Hospital
a teaching affiliate of
Harvard Medical School
http://spl.harvard.edu
Acknowledgements
Sponsors NIH: NCRR/NCI/NLM, NSF, DOD...
SPL
F Jolesz, MD, R Kikinis, MD, S Warfield, PhD,
W Wells, PhD, C-F Westin PhD, M Halle, PhD,
HJ Park, PhD, M Kaus, A Brun, D Kacher
Neurosurgery
P Black, MD, F Talos, MD
MIT
E Grimson, PhD, M Ferrant, D Gering, SM,
T Kapur, PhD, S Larsen, M Levington, PhD
SDF
D. Small, M. McKenna
http://spl.harvard.edu
Outline
Why Neuroscience Needs Grid
Computing
What we do and how we do it
How the SPL is Developing Grid
Computing
BIRN, Neurosurgery, Query Atlas
Three Predictions
The Evolution of Grid Computing for
Neuroscience
http://spl.harvard.edu
The Challenge
Get the Most Societal Benefit from
the Investment of Research Time
and Resources
Need to attract and hold the interest of
our clinical collaborators
High Quality Tools
Production-Oriented Level of Service
Need to remove obstacles that prevent
adoption – technical and cultural
Perception of Local Control
Need to Deliver Results
http://spl.harvard.edu
The SPL – Example Grid Users
Algorithm Development
Segmentation, Registration, Visualization,
Tensor Imaging
Software Development
3D Slicer: Free, VTK/ITK-based
Medical Applications – neuro and other
Image Guided Therapy: Surgical Planning,
Intra-operative Assist, Robotics, Follow-up
Basic Science Research: Developmental
Neuroinformatics, Disease Tracking (MS, AD,
Schiz), Digital Atlases
http://spl.harvard.edu
SPL Collaborations
BWH: Radiology, Surgery...
Longwood Medical Area: Children's Hospital, Harvard Medical
School, Dana Farber Cancer Inst.
Boston: MGH (Research and Clinical), MIT, BU, VA, CIMIT
National: BIRN, CISST (NSF ERC), GE, Many Individual
Collaborations
International:
Japan (AIST), Tokyo U., Osaka U.
Europe (examples)
France: INRIA Sophia Antipolis
Germany: Hamburg, Heidleburg
Sweden: Linkoping
Canary Islands
Australia: Melbourne, Sidney
http://spl.harvard.edu
Categories of Research
Analysis of Individuals
Focus Resources (Computational and Human) on
Developing and Performing Treatment
In Support of Clinicians
Analysis of Populations
Processing Pipelines to Extract Derived Data
In Support of Neuroscientists
Development of Explanations
Cross-disciplinary, Multi-scale Quest for
Fundamental Understanding
http://spl.harvard.edu
Basic Neuroanatomy
Gray Matter (Cortex)
Neuron Cell Bodies
Specialized Cortical Regions
White Matter
Fiber Tracts
Ventricles
Filled with Cerebro-Spinal
Fluid (CSF)
Highly Vascularized
Brain Responsible for 20% of
Metabolism
http://spl.harvard.edu
Diffusion Tensor Tractography
Multiple MR Gradient
Acquisitions
Three crossing fiber tracts
Sensitive to Brownian
Diffusion of Water
Cell Membranes Restrict
Diffusion
Post Processing to
Extract Probable White
Matter Tracts
Actual Tracts are Below
the Resolution of the Scan
http://spl.harvard.edu
Provided by Westin, Park, et al
Segmentation and Tractography
http://spl.harvard.edu
Segmentation: MGH; Tractography, Visualization: BWH
Developmental Neuroinformatics
a).Premature
(31 weeks)
b).Fullterm
equivalent
http://spl.harvard.edu
c).9 months
Unusual to have
the opportunity to
acquire
longitudinal data.
Images: Simon Warfield
Baby Frontal Lobe Anisotropy
34 Weeks
http://spl.harvard.edu
42 Weeks
Images: Simon Warfield
Integrated Knowledge as Atlas
Atlas construction: interactive edit of tissue
classification displayed with 3DSlicer.
http://spl.harvard.edu
Images: Simon Warfield
Pre-Operative Map
Structural
Magnetic Resonance
Imaging (MRI)
DTI
Diffusion Tensor Imaging
fMRI
Functional MRI
MEG
Magneto Encephlogram
Anatomy Atlas
“Textbook” Information
http://spl.harvard.edu
Grid Projects at the SPL
Note: Neuroscience is not Physics
or Astronomy
Where a physics paper may have
hundreds of authors, you need to argue
to justify more than 5 or 6 authors on a
clinical neuroscience paper
Clinical collaborators come from a
'descriptive science' background
A new grid paradigm is needed to
support the next generation of
neuroscience research
http://spl.harvard.edu
BIRN
Calibrated Acquisition
Consistent and Well-Curated Archive
Raw Data, Human Subject Data, Derived Data
Federated Data Grid, Subject Protection
Software Interoperability Among Flagship
Analysis and Visualization Tools
Migration of Key Computation Tasks to
Teragrid
http://spl.harvard.edu
BIRN Challenges
Technology
Large Data Sets: Storage, Archive, Retrieval
Network Infrastructure, Data Grid/Compute
Grid, Software Infrastructure
Science
Multi-site Images: Calibration, Standardization
Clinical Scales / Terminology
Governance
Joint approach to IRB/HIPAA
Data Sharing (Ownership, Publications)
Sociology: Encouraging Collaborations, IP,
Accountability, Credit ...
http://spl.harvard.edu
BIRN Achievement
A Working National Collaboration in a
Field without a “Big Science” Culture
Even with the best intentions and investigators
who really believe in the benefits, sharing is
hard
http://spl.harvard.edu
Morphometry BIRN
SRB
GE
Siemens
Picker
DICOM
Files
Philips
Sort Images
Deidentify
Go/NoGo
- Standard
-Deface or Mask
- Clean DICOM
Header
- Render Movies
-Display Movies
- QA Approval
of Defacing
BWH/MGH
Duke
Directory
Hierarchy
- Identify
Deface
Series
UCI
Conversion
to
DICOM
Retrospective
Data
Archives
(various
formats)
http://spl.harvard.edu
Institutional Firewall
Upload
-Extract
Metadata
- Optimize
for SRB
UCSD
Facial Deidentification
http://spl.harvard.edu
Courtesy MGH
Morphometry Calibration
Siemens
GE
Same Subject
Co-registered
No Distortion Correction
With Distortion Correction
Improved across site test-retest reproducibility:
alignment of cortical surfaces improves
with distortion correction
http://spl.harvard.edu
Courtesy MGH
Function BIRN
Calibration Methods for Multi-Site fMRI
Study Regional Brain Dysfunction
Progression and Treatment of Schizophrenia
Human Phantom Trials
Common Consortium Protocol
5 Subjects Scanned at All 11 Sites
Add'l 15 Controls, 25 Schizophrenics Per Site
Statistical Techniques
Identify Cross-Site Differences
Develop Corrections to Allow Data Pooling
Develop Interoperable Post-Processing
http://spl.harvard.edu
Function BIRN
Patients with schizophrenia show abnormal
modulation of temporal and frontal regions during
semantic processing
fMRI response at 3T and 1.5T with identical
software and hardware platforms (GE SIGNA)
Harvard-MGH
Stanford – Lucas Center
http://spl.harvard.edu
BIRN Query Atlas
Morphometry
Analysis
Converts Raw
Image Data into
Symbolic form
that Allows
Complex
Queries
Can Overlay
Population or
Subject Data
http://spl.harvard.edu
Segmentation: MGH; Visualization: BWH
BIRN Query Atlas
3D Environment for
Neuroscience Queries
Identify Studies
Drill Down
Population Statistics
Atlas/Reference
Mouse Homology
Cellular/Genetic
Databases
Taxonomy/Homology
Gene Expression
Protein Localization
Web
Entrez...
http://spl.harvard.edu
Real-Time Brain Mapping
Concentrate All Computing
Power to Treat a Single
Patient's Condition
Provide Surgical Team with
Information-Rich
Environment
Pre-Operative Images and
Analyses
Quantied Intra-Operative
Changes
Integrated Visualization
http://spl.harvard.edu
Images Provided by Kaus
Clinical Problem: Brain Tumors
Many Tumor types
Glioma, Astrocytoma, etc...
Treatment
Craniotomy
Excision
Goal: Preserve Brain
Function
The “Eloquent” Cortex
White Matter Tracts
Blood Vessels
http://spl.harvard.edu
MRT
Open Magnet
Operating Room
Tracked
Instruments
Image Guided
Therapy
http://spl.harvard.edu
Image Guided Neurosurgery
Pre-op Map and
Surgical Plan
Superimpose on Real
Patient Position
Use Tracked
Instruments to Perform
Surgery
http://spl.harvard.edu
Provided by Leventon et al.
Issue: Brain Deformation
Centimeter Scale
Displacement
Pressure Changes
when Skull is Opened
Surgical Manipulations
Retractors
Excision
“Living” Responses
Anesthesiology
Inflamation
http://spl.harvard.edu
Finite Element Method
3D Mesh Generation
Intra-Operative
Surface
Used as Boundary
Condition
Deformation Field
Bring Pre-Op Brain Map
to Intra-Op Configuration
http://spl.harvard.edu
Mesh Generation
Initial Uniform
Tetrahedral Mesh
Clipping and
Remeshing
Adaptive Refinement
http://spl.harvard.edu
Material Models
Current Software
Homogeneous, Linear Elastic
100K Nodes
Real Brain is Highly
Nonlinear
Inhomogeneous,
Hyperviscoelastic, Anisotripic
Material Properties
Change
Remember: Patient is Alive!
http://spl.harvard.edu
Computational Problem
Bottleneck is Solving FEM
High Resolution Models
Accurate Material Models
In Clinically-Appropriate Timeframe
Iterated Large Sparse Matrix Solutions
Numerically the Same as Classical
Nonlinear FEM Problems in Engineering
Analysis
http://spl.harvard.edu
Surgical Timeline
Before Surgery
During Surgery
Pre-Operative Mapping
Intra-Operative MRI
Rigid Registration
Tissue Classification
Surface Displacement
Biomechanical Simulation
Visualization
Surgical Progress
|
0
|
5
|
10
Time (min.)
http://spl.harvard.edu
|
15
|
20
|
25
|
30
|
35
|
40
|
45
|
50
|
55
|
60
Prediction 1
Grid computing will really take off once
users don't have to know they are using it
Meaning: don't expect analysis programs will be
re-written with grid APIs.
Instead, hide grid APIs behind the APIs people already
use
Example: Freesurfer datasets
Over 1G per subject, hundreds of files
Dozens dozens of executables and analysis paths
Freesurfer implicitly relies on the OS/NFS to manage
caching
Grid implementation must provide the interface layer
Hundreds of programs fit this model
http://spl.harvard.edu
Prediction 2
Automated Image Segmentation will be the
first big success for the Grid in medical
imaging
Current paradigm is still “one student, one
computer, one dataset”
Ability to test algorithms on large-N of studies in
parallel will qualitatively change the practice
Key Challenge: Human-in-the-loop is still required
for high quality segmentation
http://spl.harvard.edu
Prediction 3
Simulation will be the killer app
Simulation is needed to integrate our
understanding of multiple layers of biology
Truly computationally demanding
Digital Humans, Virtual Patients, “Body Double”,
Holomer... to Allow Tracking of Patient Condition
Over Time
Model-Based Pathophysiology to Predict Disease
Course and Treatment Response
Example: New DARPA Virtual Soldier Project
U Mich, U Utah, UCSD, U Wash, Stanford, ORNL,
Simquest, Mission Research, GE Research and Brigham
and Women's
http://spl.harvard.edu