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![Page 1: SCI Utah Interactive Simulation and Visualization in Medicine Chris Johnson Scientific Computing and Imaging Institute School of Computing University of.](https://reader035.fdocuments.net/reader035/viewer/2022070407/56649e115503460f94afcd38/html5/thumbnails/1.jpg)
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Interactive Simulation and Visualization in Medicine
Interactive Simulation and Visualization in Medicine
Chris JohnsonScientific Computing and Imaging
Institute
School of Computing
University of Utah
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SCI Utah
Construct a model of the physical domain (Mesh Generation, CAD)
Apply boundary conditions
Numerically approximate governing equations (FE, FD, BE)
Compute (Preconditioners, Solvers)
Visualize (Isosurfaces, Vector Fields, Volume Rendering)
Computational Science PipelineComputational Science Pipeline
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Computational Science - TodayComputational Science - Today
Modeling
Simulation
Visualization
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SCI Utah
Computational Science - TodayComputational Science - Today
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ViSC Workshop ReportViSC Workshop Report
“Scientists not only want to analyze data that results from super-computations; they also want to interpret what is happening to the data during super-computations. Researchers want to steer calculations in close-to-real-time; they want to be able to change parameters, resolution or representation, and see the effects. They want to drive the scientific discovery process; they want to interact with their data….”
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Computational Science - Tomorrow?Computational Science - Tomorrow?
Modeling
Simulation
Visualization
userguides
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Computational Science -Tomorrow?Computational Science -Tomorrow?
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Computational SteeringComputational Steering
If this is so great, why is it just starting to “catch on”?• Scientists greedy for CPU cycles• Faster machine - Larger problems• Different sets of expertise• It’s hard to make it all work well!
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SCIRunSCIRun
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Application NeedsApplication Needs
What if questions (a “computational workbench”)
Iterative design (medical device design)
Time-critical (diagnosis, surgery)
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“Minor” Challenges“Minor” Challenges
Accommodating parallelismLarge data setsComplex physics/physiologyExisting code3D user interactionEfficiencyFault tolerenceEtc., etc., etc.
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Device Design: DefibrillationDevice Design: Defibrillation
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Time-critical: NeurosurgeryTime-critical: Neurosurgery
Harvard & Brigham Women’s Hospital
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Interactive Large-Scale Visualization Interactive Large-Scale Visualization
Medical
Scientific
ComputingGeoScience
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Visualization at All LevelsVisualization at All Levels
Application level• Streamlines, cutting planes, isosurfaces, surface maps, etc.
System level• module profiling• memory allocator visualization• more in progress...
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ConvergenceConvergence
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Numerical FeedbackNumerical Feedback
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3D Widgets3D Widgets
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Real-Time Ray TracerReal-Time Ray Tracer
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SCI UtahMaximum Intensity Projection
Maximum Intensity Projection
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35 million spheres35 million spheres
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Adaptive Finite ElementsAdaptive Finite Elements
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SCI Utah
Adaptive Finite ElementsAdaptive Finite Elements
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Time-dependent AdaptationTime-dependent Adaptation
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SCI Utah
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Geo Science ApplicationGeo Science Application
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ASCIASCI
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C-SAFE Uintah NetworkC-SAFE Uintah Network
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ASCIBlue Mountain
Los Alamos National Lab
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GoalsGoals
Help foster interest/research in PSEs/Components• Computational Workbench
Help realize a common API for PSEs/Components• Common Component Architecture (CCA) Forum www.acl.lanl.gov/cca-forum
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SCI UtahThe frustration of using bad software…The frustration of using bad software…
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SCI Utah
SCIRun PortsSCIRun Ports
Requirements:• OpenGL• Tcl• p-threads
Unix• single and multiprocessors
PC - NT and Linux
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SCIRun AvailabilitySCIRun Availability
Not generally available yet
Approx. 10 beta users now
Research version available as soon as we finish documentation
Commercial license available from Visual Influence:
www.visualinfluence.com
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ConclusionsConclusions
Computational steering (interactive computing) can be a more efficient paradigm for iterative design problems and time-critical computational problems
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Future WorkFuture Work
Detachable User Interfaces
Distributed Memory Implementation
More Modules
New Applications
Finish Documentation!!
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AcknowledgementsAcknowledgements
DOE ASCI
NSF PACI and PFF
SGI Visual Supercomputing Center
Utah Centers of Excellence
Visual Influence
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Applications 1Applications 1Mark Ellisman – UCSD, NPACI, NCRR
• Linking expensive data acquistion devices – high resolution microscopes
• Compare data from the microscopes with data from simulation and databases
• Data size – 2K^3 (will be 4K^3 within a couple of years) – lots of computing – currently using distributed workstations using Globus
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Mark Ellisman - contMark Ellisman - cont• Time critical because of mass loss• Data -> Modeling ->Analysis ->Visualization->Database->Feedback
(and feedforward) throughout Could enable further
science/applications with protein structures (and others)
Useful for extending (via simulation and/or experimentation) functional information within multilayer databases
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Joel SaltzJoel Saltz
U. Of Maryland, Johns HopkinsAlpha Project (NPACI) with Mary
Wheeler (UT Austin) on reservoir simulation
Tighten the loops between production information, sensor data and simulation data.
Satellite data, classification, visualization, large-scale data query and processing.
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Joel Saltz - contJoel Saltz - cont
Patient specific diagnosis and treatment – need to access and register distributed data, integrate radiology, microscopy, pathology data
Applications in drug delivery, interventional radiology, etc.
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John MillerJohn MillerCenter for Computational Biology –
Montana StateFigure out how the brain worksHow information is encodedSenors – receive data, coupled with
information from a large database, then via a combination of experimental and simulation data, control parameters to manipulate the system (the visual system, for example)
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John Miller - contJohn Miller - cont
Massive data streams – analyze on the fly – use this data to interact with a model drawing parameters from databases – and do it VERY fast.
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Avis CohenAvis CohenLong running simulations for
stochastic differential equations – doesn’t need to be interactive.
Spinal cords – chips that stimulate spinal cords in adaptive way such that it can take sensory feedback and maintain a particular motor pattern.
Use an adaptive analog systemUnderstanding algorithms for
input/output of systems
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Carlos FelippaCarlos Felippa
Aerospace engineeringMultiphysics, embedded systems
with real-time controlReconfigurable systemsHeirarchical systemsModel systems and controlRobust against uncertaintyFigure out commonalities
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SCI Utah
Michael CreutzMichael Creutz
Particle physicist
Long running computational jobs
Visualization not useful (yet)
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Charbel FarhatCharbel Farhat
Univ. of Colorado – Aerospace engineering
Data driven embedded systemsFeedback control of embedded
systems – need automatic system for a control
Autocallibration between experimental apparatus and simulation
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Sandy BoysonSandy Boyson
University of Maryland
Currently there are long (weeks) delays in market feedback/analysis
Situational awareness sensory data of Army troops – what to do with all the data, how to use the data for real-time response – how do you manage such situations
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Abhi DeshmukhAbhi Deshmukh
U. Of Mass.
Distribution systems of transportation networks, getting feedback from people on the road and planning shortest path
Using algorithms based upon how ants find food
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Jacobo BielakJacobo Bielak
CMU – seismology Need a heirarchy of problems
and methods/techniquesSome are real-time, some aren’tWhat if questionsDesign questionsReal-time (related to prediction
and control)
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Jacobo Bielak - contJacobo Bielak - contEarthquake ground motionIs there a design criteria based upon
ground motion (not real-time at this time)What if – how does the underlying structure
relate to ground motion – does it depend upon the local of the source.
Why now? – more/better/cheaper sensors and integration with simulation
Distributed collaboration, steering, and visualiziation
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SCI Utah
Daniel WeberDaniel Weber
U. Of OklahomaCenter for Analysis and Prediction of
StormsNumerical weather prediction
systems – large-scale (1000 CPUs)Run at higher resolution (1K on a
side)Registration of multiple modalities of
input data (radar, doppler, etc)Actually works
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Daniel Weber - contDaniel Weber - cont
Feedback – cycling using updated data
This is the right time to take the next step (entire US)
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Robert LodderRobert Lodder
Univ. of Kentucky – cardiac catherization
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Common ThemesCommon Themes
Hardware Needs:• Need more cycles• Need more bandwidth
Software Needs:
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SCI UtahDecision process
• Objective functions• Value of information• Treatment of uncertainty• Perception• Stopping rules
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New ApplicationsNew Applications
Radio astronomy – 90 radio telescopes – full sky survey – pulsars, comets, variable stars – are time-dependent – need a tight connection between data collection, then move the specific type of telescope towards the location – must have dynamic link or experiment doesn’t work.
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What Will DDDAS Enable?What Will DDDAS Enable?
Better weather prediction because of data feedback
Enable new level of physiological experiments because of the tight coupling between analysis and experiment – this would alter the way some experiments are done
Next level of embedded systems – ability to react to uncertain or unpredictable input
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Why Now?Why Now?
Leverage existing NSF programs
Think tactfully about implementation of new programs
Networking/interconnectivity, cycles, disks, and new algorithms are enabling new applications
New sensors/data is available
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SCI UtahDynamic Data-Driven Application SystemsDynamic Data-Driven Application Systems
Applications Group IChris Johnson Greg McRae
Jacobo Bielak Sandy Boyson
Janice Coen Abhi Deskhmukh
Mark Ellisman Robert Lodder
John Miller Joel Saltz
Klaus Schulten Carlos Felippa
Avis Cohen Charbel Farhat
Michael Creutz Daniel Weber
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Bad DayBad Day
Anonymous ftp to:
sci2.cs.utah.edu
cd to /pub
Download badday.mpg
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DDDAS MotivationDDDAS Motivation
Reduce time to adapt to new conditions and to decide how to allocate resources to respond to the change• Experiments on short-lived processes
(e.g. physiology)• Capture sporadic astronomic events • Active control of structures during an
earthquake• Disturbances in a chemical plant• Early warning systems (fire, tornado,
earthquakes, hurricanes, pollution, floods)
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DDDAS Motivation – cont.DDDAS Motivation – cont.
• Financial and management systems (supply-chain coordination)
• Crisis management (terrorist attacks, epidemics)
• Adaptive structures (car suspension, buildings, space structures)
• Autonomous systems (decision processes)
• Interactive system analysis and control of experiments
• Predict extreme geospace conditions (space weather)
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OverviewOverview
Model BuildingModel Building
Simulation/AnalysisSimulation/Analysis
VisualizationVisualization
SensorsSensors
KnowledgeManagementKnowledgeManagement
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Data Driven System CharacteristicsData Driven System Characteristics
Real-timeFeedback and Control (closing
the loop, robust)How uncertainty controls the
output and parameter selection (sensitivity analysis)
Model reductionRelationships to sensors
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Data Driven System CharacteristicsData Driven System Characteristics
Predictive modeling (combinations of hardware and software)
Better techniques to solve large-scale inverse problems (inverse correlation)
Relationships between space/time scales and measurements
Computational Workbenches
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DDDAS Adaptive ObservationDDDAS Adaptive Observation
Infusing data into the simulation and improving the model for the next simulation• Understand where errors are and understand where more data is needed
• Understand where to get the initial conditions
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Why Now?Why Now?There is a convergence of computing,
networking, algorithmic, sensor, software, and application technologies. Integration of these technologies affords taking “the next step” in many application areas.• Can’t do the kinds of experiments unless one can
interact with large systems (for example – neuroscience)
• Use simulation more than a posteri way – DDDAS can move us beyond that
• Now we have computational resources (hardware and software) to approach realistic problems
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But…But…
Artificial department boundaries are an impediment to creating needed expertise
Computational Science at NSF is not well defined
Sociologically issues with regard to the interaction of theory, experiment, and computation
Education/training a large issue for computational scientists
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Enabling TechnologiesEnabling Technologies
Model BuildingAlgorithmsSensorsComputational systemsVisualization and analysisDatabase management systemsCommunicationsIntegration software
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AlgorithmsAlgorithmsMathematical development
• Improved Bayesian methods for model-based experimental design, parameter estimation, state estimation, sensor placement
• Inverse methods for large-scale integro-partial differential equation
• Identification of time-varying systems• Uncertainty propagation• Time-series analysis• Solution of large-scale nonlinear
programming problems
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Sensors/ActuatorsSensors/Actuators
Can dramatically change the way one looks at a problem, but requires interaction across many disciplines to build and use them, e.g.• Chemical lab on a chip• Molecular markers• Noninvasive (and very invasive) physiological
monitoring • Microelectronics (smart materials)• Remote sensing• Adaptive optics (multiple mirror telescopes)• Particle tracking• Damage detection
All have high data rates
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VisualizationVisualization
Interactive visualization techniques for large data• Graphical user interface design• Haptics, visual and other feedback
mechanisms• Scientific and higher dimensional data
streams• Distributed collaborative visualization
(workstation and VR)• Remote visualization (compression,
view dependent, perception-based)
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Data ManagementData Management
Need to interact and manage large data• Visual databases• Distributed databases• Interaction• Legacy (heritage) databases• Develop of tools for supporting interactive
dataset manipulation • Tools to couple simulations to databases• Merging different measurements of the
same process (e.g. registration)
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CommunicationsCommunicationsCommunication (between humans and
machines) infrastructure to facilitate interaction (both locally and remotely) and to expand the potential for collaboration (between humans)• Bandwidth (more) management (connect
adaptively to systems)• Compression technologies (feature detection,
multiresolution, etc.)• Fast wireless and distributed sensors• Sensors that send out upon need and/or demand• Smart sensors that compute locally and send
updated/changed information
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Integration SoftwareIntegration Software
Encourage open sourceCommon API (to software and to
sensors)Common component software
architectureDealing with heritage codesRole of filters and wrappers
(scripting languages, etc.)
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Related Reports and InitiativesRelated Reports and Initiatives
1998 NSF Workshop on PSEs (Abdali)1998 DOE Report on Large Data
Visualization1999 NIH Report on Biomedical
ComputingModel Based Simulation –
caswww.colorado.edu/MBS.Workshop.d/index.html
DOE ASCI ProgramPITAC Report
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Industry RelationsIndustry RelationsStudents (although we need
cooperative programs to allow students to finish degrees)
Spawn new industries and multi-industry collaborations
Tighter connections between industry data output and use in academic models/simulations (airlines, weather, FAA example)
Pricing models based upon need/consumption
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ImplementationImplementation
DDDAS is cross/multi-disciplinary in nature!Don’t implement in ITRCross directorate reviewing required Need to figure out computational science
within NSFNeed all directorates on boardNeed LOTS of $$Some projects beyond the current 3-5 year
limitsBalance the risk portfolio to include more
speculative endeavors
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SCI UtahDynamic Data-Driven Application SystemsDynamic Data-Driven Application Systems
Applications Group IChris Johnson Greg McRae
Jacobo Bielak Sandy Boyson
Janice Coen Abhi Deskhmukh
Mark Ellisman Robert Lodder
John Miller Joel Saltz
Klaus Schulten Carlos Felippa
Avis Cohen Charbel Farhat
Michael Creutz Daniel Weber