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Quick overview

Quick overview. Robert G. Belleman, Ph.D University of Amsterdam Email: robbel@science.uva.nl. The Grid .vs. the WWW. The WWW is about sharing information. The Grid is about sharing resources.

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Quick overview

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  1. Quick overview Robert G. Belleman, Ph.D University of Amsterdam Email: robbel@science.uva.nl

  2. The Grid .vs. the WWW The WWW is about sharing information. The Grid is about sharing resources. databases, web pages, high performance computing systems, networks, data acquisition equipment, data storage / archiving and retrieval facilities, algorithms, search engines, images, …

  3. Applications Applications Applications App Toolkit App Toolkit App Toolkit The Grid .vs. the WWW The WWW is about sharing information. The Grid is about sharing resources. Applications Grid services layer Grid “middleware” Grid fabric layer

  4. Applicationspecific services Application genericservices & VL-eservices Virtual Laboratory rapid prototyping (interactive simulation) Additional Grid Services (OGSA services) Grid & Network Services VL-eCertification Network Services (lambda networking) VL-eExperimental Environment Virtual Laboratory for e-Science Bio-informatics Medical Applications Telescience Virtual Laboratory middleware Grid Middleware SURFnet VL-eProof of ConceptEnvironment

  5. Partners Philips Intera 3T MRI scannerAMC, Amsterdam MEG scannerVUmc, Amsterdam

  6. Why Grids? Why VL-e? • Integrated access to distributed computational resources, irrespective of time and/or location.For example: • High Performance Computing • High throughput, low-latency computing • High Capacity Storage • Remote PACS, shared data access • Medical image data and patient records • Longitudinal/epidemiological studies, atlases • Algorithms • Simulations, image processing, visualization • People • Collaboration, 2nd opinions, remote diagnostics • ...

  7. 2D/3D visualization VL experiment topology Image processing,Data storage Medical Problem Solving Environment Data retrieval,acquisition Filtering, analyses,simulation

  8. Storage Resource Broker (SRB) • UvA, AMC, SARA • SRB provides: • Grid-based access to distributed storage resources • Access to storage services at SARA • On- and near-line access • 1.2 PBytes, soon more • Flexible access • Unix, Windows, web browser • Metadata support (!) • Trial between AMC, UvA and SARA started • to relay AMC fMRI data to researcher workspot • to archive research data

  9. http://www.vl-e.nl/

  10. The data sharing potential • Collaborative scientific research • Information sharing • Metadata modeling • Allows for experiment validation • Independent confirmation of results • Statistical methodologies • Access to large collections of data and metadata • Training • Train the next generation using peer reviewed publications and the associated data

  11. Partners, people and collaborations • Philips Medical Systems, MRI • de Boer • Hoogenraad • AMC, fMRI • den Heeten • Grimbergen • Snel • physicist • Philips NatLab, SWA • Bucur • Obbink • 2 scientific programmers • UvA • Belleman • Olabarriaga • PhD stud. • TU Delft • de Vos • Botha • 2 PhD stud. • VUmc, MEG Centrum • van Dijk • de Vos • Cover • IBM • …

  12. Domain decomposition • local image operations • isosurface extraction • ... • Computational decomposition • global image operations • fiber tracking • ... • Functional decomposition • composite systems • storage • interactive systems • ... Grid Architecture for Medical Applications (GAMA) • UvA & Philips Research/SWA(Anca Bucur)

  13. Brain Imaging and Fiber Tractography • AMC, Philips, TUD, UvA • Diffusion Weighted Imaging (DWI) • Restricted Brownian motion results in anisotropy that can be measured • >= 6 measurements, reduced to tensor per voxel • Largest eigenvectors give diffusion vector • Whole volume fiber tracking can takemany hours • Depends on size of volume andnumber of measurements per voxel • Suitable for parallelization

  14. MRI scanner LUMC AMC Patient’s vascular geometry(MRA) MRI scanner Simulated “Fem-Fem” bypass Diagnosis and surgical planning • Simulated vascular surgery: • Computer Aided Diagnosis • Computational Fluid Dynamics • Surgical planning

  15. Framework for Automated Image Processing and Routing (FAIPR) • AMC, dr. Jeroen Snel • Seamless integration of special-purpose services: • Matched Masked Bone Elimination (MMBE):CT based method, currently in use for early detection of aneurisms in brain scans • MR perfusion analysis:Off-line image processing using AMC post-processing library • Caching of fMRI studies:Allows researchers to access data through SARA SRB

  16. 3rd party libraries • Consolidated access • AMC post-processing library • Visualization toolkit (Vtk) • Insight segmentation and registration toolkit (Itk) • FMRIB Software Library (FSL) • Virtual/augmented reality toolkits (ARToolkit) • Applications • Image processing • Advanced interactive visualization • Image guided surgery

  17. Visualization Toolkit (Vtk)

  18. Resource at domain 1 Resource at domain 2 Resource at domain 3 Resource at domain 4 Library of components Process Topology editor Medical Problem Solving Environment Resource brokeringand allocation Objecttrackingsystem Renderingengine Volumerenderer Remoterenderingserver

  19. Pittsburgh The markers are tracked byinfrared cameras The positions are transmitted to the visualization system The new image is transmitted to the display 10 Gigabit/s path on the SURFnet and Abilene networks The visualization system uses the reported positions to render a new image of the visualized data Amsterdam SGI Onyx4 at SARA Co-located interactive 3D visualization The volumetric data resides locally on the visualization system

  20. SC2004 “Schrödinger’s Cat” demo SuperComputing 2004, Pittsburgh, Nov. 6 to 12, 2004 Produced by: Michael Scarpa Robert Belleman Peter Sloot Many thanks to: AMC SARA UvA/AIR GigaPort Silicon Graphics, Inc. Zoölogisch Museum NCF

  21. parameters/settings, algorithms, intermediate results, … software packages, algorithms … sensors, detectors,amplifiers, … raw data processed data presentation acquisition processing sensors, imaging devices,databases, protocols, … conversion, filtering,analyses, simulation, … visualization, animate,interactive exploration, … Rationalization of the experiment and processes Metadata Issues for a reproducible scientific experiment experiment results Much of this is lost when an experiment is completed.

  22. Definition of experiment protocols • Workflow definitionsRecreate complex experiments into process flows • Workflow executionMaintain control over the experiment • Analyses process definitionsTopologies of data processing modules • InterpretationVisualization of processed results Ontology definitions can help in obtaining a well-structured definition of experiment, data and metadata.

  23. COMMENTATOR (LINK) COMMENTATOR (LINK) isMadeBy (LINK) isMadeBy (LINK) EXPERIMENT COMMENT EXPERIMENT (LINK) COMMENT (COPY) (LINK) (COPY) COMMENT PROPERTY PROPERTY (COPY) (COPY) (COPY) hasNextExperiment hasNextExperiment (NOREUSE) hasComments (NOREUSE) hasComments (COPY) hasComments hasProperties hasPrevExperiment (COPY) hasProperties (COPY) (COPY) hasPrevExperiment (NOREUSE) (COPY) (NOREUSE) hasSteps hasNextStep isPartOfProject hasSteps (NOREUSE) (NOREUSE) (NOREUSE) (NOREUSE) ARRAY PATIENT PROJECT EXPERIMENT PATIENT MRI MEASUREMENT (LINK) (COPY) MEASUREMENT (COPY) (COPY) hasExperiments hasPrevStep isPartOfExperiment (NOREUSE) (NOREUSE) isPartOfExperiment (NOREUSE) (NOREUSE) hasOwner hasOwner LINK hasPerformed isPerformedBy LINK hasPerformed (NOREUSE) isPerformedBy (LINK) ownsExperiments (NOREUSE) hasContributors (LINK) ownsExperiments (NOREUSE) (LINK) (NOREUSE) contributedExperiments (NOREUSE) OWNER OWNER OWNER (LINK) OWNER (LINK) (LINK) (LINK) CONTRIBUTOR (LINK) Ontology 2 Standards • Ontologies: • Defining an ontology is difficult • Merging ontologies is even more difficult, butit’s the key to expanding a knowledge base It’s important to define standards early! Merging ontologiesresults in more thanthe sum of its parts. Ontology 1

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