Breakout Session
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Interactive Data Exploration and
Visualization
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Exploring Large Data Sets
Collaborative Control and Analysis
Teaching Support
Remote Consultation
Surgical Planning
Access Control Policies
http://www.spl.harvard.edu
Particpants
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Kazunori Nozaki – Osaka U – Dentist, CFD
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Chao Quan Chen – Edinburgh Med Ultrasound Processing 300 MB
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Martin Connell – Edinburgh – Visualization, Processing...
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Cecilia Germain – Computer Research Lab Paris – Online
Radiology Grid
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Nick Avis – Cardiff U – Computational Steering, large scale viz
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Daniel Hanlon – Daresbury Laboratory – Globus/grid/portals – med
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Sofie Norager – EC – HealthGrid Merging Biomedical informatics
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Richard Ansorge – U Cambridge – Cancer care telemedicine, viz
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Steve Pieper – SPL / BIRN – Surgical Planning, Viz
http://www.spl.harvard.edu
Applications
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Therapy Planning
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Intra-operative sugical assist – interactive
IMRT (Radiation Treatement Planning) –
medium term
Volume measurement – continuum from small
interaction/large computation to large
interaction/computation.
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Interactive Rendering
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Volumetric segmentation
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Adaptive control of instrumentation
http://www.spl.harvard.edu
Categories of Visualization
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Today
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Simple Tabular / Histogram
Static Rendering
Movies
Large Data Model – Astro, CFD -> MEG,
Want to have
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Interactive Static Data Set
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Interactive Dynamic Data Set
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2D, 3D, 4D, Multi-Modal, Populations, Multi-Scale...
Dynamic – rendered on the fly
Simulation/Calculation on the fly
Computational Steering
Tiled / distributed rendering
http://www.spl.harvard.edu
Middleware Requirements
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Scheduling
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Priority Allocation
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Administrative control of priories
3 Levels of task types
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Low – scheduling small tasks on grid
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Medium – like the scheduler itself
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Large – workflow (classical grid – globus, grid
services)
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Quality Of Service
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Auditing of resource usage
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Level 2 Grid – some hardware is being dedicated to
grid use, with interactive applications being allowed
http://www.spl.harvard.edu
Enabling Tools
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VNC 
xf4vnc.sf.net – GLX enabled vnc with tight vnc
for unix
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Shared whiteboards
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Videoconferencing – e.g. Tandberg
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NAG Explorer – grid enabled
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SGI viz server
http://www.spl.harvard.edu
User Requirements
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Need More Example Applications
Performance Evaluation of all levels of
the programming stack
What is the overhead of Grid Services?
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Methods to determine type of service you are
connecting to and choose alternate
implementations depending on type of service
Comparison to dedicated cluster approaches to
same problems
Intelligent/Selective Data Access
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Standard file system API
Move to Database as API
http://www.spl.harvard.edu
Scenario
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Migrate to new grid architecture from
locally controlled machines
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Perception of local control
Redundancy of compute resources provides
better uptime than local machine
http://www.spl.harvard.edu
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Market economy of resources
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Comparison with other resource markets (e.g.
Electrical power)
Need an overabundance of resources so
that user needs can be accomodated in
worst-case
What are commercial companies doing?
http://www.spl.harvard.edu
Conclusions
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Grid is not ready for interactive use yet
http://www.spl.harvard.edu
Projects
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Distributed rendering techniques for large
data using grid resources
Multiple data types integrated in the
rendering
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Text
2D images
3D volumes, vectors, tensors, tracts
Models
Annotations
dynamic/kinetic models – metabolism, function
Error metrics displayed in view
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Data from multiple simulations, instruments
Parameter searches
http://www.spl.harvard.edu
Projects (Visual Semantic Grid!)
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Queryable Visual Elements
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BIRN Query Atlas Prototype – brain cortex
parcelation click to launch web search
Every object is a hot link to data about itself
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“User profiles” to steer user to types of
information (e.g. students vs. clinicians)
Scalability to large numbers of users
Multilingual
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Metadata, provenance, source (sql or url from which it
was loaded or best way to access it)
Native language, technical vs non-technical
Local expert translators
Homologies between schools of thought
(mediator)
http://www.spl.harvard.edu
Remote Resources on Confidential
Data
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Scenario
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Clinican queries PACS for longitudinal patient
data and sends dicom files to grid for registration
and analysis
How to keep the whole compute transaction
secure even when calculations happen remotely
Also important to industrial applications (e.g. Big
pharma)
http://www.spl.harvard.edu
Issues
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More European / Asian experts could be
drawn on
Physics problems are different
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Better defined constraints on amount of data
Off-line computations, data reduction
Who do you trust?
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How to make the social/administrative parts of
the grid authentication work (e.g. account
creation)
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European datagrid project may have answers
http://www.spl.harvard.edu
Paper Topics
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Visualization Taxonomy
API types to support “weights” of
processing types (batch vs. function call)
XML Schema for Semantically aware
visual elements
Caching/Random Access to Data
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Resource aware applications / proxy
With application override possible
http://www.spl.harvard.edu
What is limitation of grid?
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Justify conclusion that grid is not yet
suitable for interactive visualization
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What is overhead for grid services? What
optimizations are possible?
What are computer center policies that limit or
enable interactive applications? What are the
reasons for those policies?
What hardware limitations influence ability to use
grid resources interactively? Swapping?
Do the cluster OS and queing systems allow
pre-emptive multitasking?
http://www.spl.harvard.edu
Testbed
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Example code of grid aware visualization
Software that can display the other
testbed data
http://www.spl.harvard.edu
Other Groups
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Visualization needed for other groups
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With reference to our taxonomy
Expert review QA
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Radiologist interpretation – unknown “gold
standard”
http://www.spl.harvard.edu
Paper Outline for Visualization
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Scenario
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Generic
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Locally+Remotely Generated Large Dataset
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Remote Compute Resource Required
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Secure Communications
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Interactive Response / Computational Steering
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Potentially Large Output to Visualize/Interpret/Archive
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Radiology Specific for Clinical Research
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CT/MR/US/PET... longitudinal disease tracking
5D data sets, multi-component images
Image Standardization / Calibration
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Inter-, Intra-subject/modality registration
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Accommodate instrument differences – seek state of the art techniques
Patient confidential information protection
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Security or Deidentification
Interaction
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Starting pose; Non-rigid registration needs help avoiding local minima
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Remote consultation and collaboration
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Data streaming of local and remote data
http://www.spl.harvard.edu
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System Requirements
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On-demand computing – across weights of compute jobs (batch, in-between, interactive)
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Reduce overhead for initiating grid services (authentication, resource descovery...)
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Virtual Overabundance of Resources
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Pool IT budgets from individual researchers and share among groups
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What you need is almost always available to you
Testbeds
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An instance of the interaction scenario either in registration or measurement
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Involved
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Clinical sites
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Application software sites
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Computer resource sites with grid infrastructure expertise and resources that can be
dedicated to interaction experimentation – and with links to the grid infrastructure
development groups to work on system requirements groups
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(Optional for Discussion: industrial partner)
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IBM, Sun, Dell...
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GE, Siemens, Philips...
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Big Pharma...
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Others...
http://www.spl.harvard.edu
Issues of Group
http://www.spl.harvard.edu
Slicer - http://www.slicer.org
Flagship Application of the
SPL
VTK, C++, Tcl/Tk, OpenGL
Modeling/Visualization
Platform
Neuroscience Research
Surgical Planning
Source Available and Free
for Non-Clinical Use
http://spl.harvard.edu
Exploring Large Data Sets
Example: Keyhole
Seamless transitions across multiple
scales
Intuitive User Interface
Displays where data exists and
where not
Integration of Imaging and nonimaging data
Multiple ways to find the data of
interest (by feature type, name,
address, interactive exploration...)
How to Adapt/Expand this to
Medical Images?
http://spl.harvard.edu
Query Atlas Interface Project
An interface to interact
with the BIRN database
as easily as Keyhole
interacts with the earth
data
Draw on skills of SDF
(award-winning
information design firm)
in genomics/proteomics
visualization
http://spl.harvard.edu