Grid Computing T h l f Technology for

Medical ApplicationsMedical Applications

Mohsen GuizaniKuwait University and

Western Michigan Universitymguizani@ieee.org

Contents

Grid ComputingMedical applicationsMedical applicationsSecurityPossible SitesFuture of GCFMAsPossible Challenges of GCFMAs

What is a Grid?

A Grid is a hardware and software infrastructure that clusters and integrates high-end computers, networks,computers, networks, databases and scientific instruments from multiple sources to form amultiple sources to form a virtual supercomputeron which users can work collaboratively within virtual organizations.

What is Grid?Resource sharing between organisations (Resource=

computer, data, instrument, storage,..)Share investmentShare investment

many small computers = one high throughput architectureMeet occasional peak demands with large number of resourcesEnable access to specific significant resources

E.g. from national investment in data or computeE.g. from national investment in data or compute resources

Allows communities with small budgets means to access significant resourcesaccess significant resources

Orchestrate Multiple pResources

E g Enabling whole system approach to complex problems

computers

E.g. Enabling whole-system approach to complex problems

software

sensor nets

Grid

instruments

Shared data archivescolleagues

Diagram derived fromIan Foster’s slide

Development Framework

Typical Grid Topology

INTERNET

Some Grid Projects

■ Asia: I-Grid (India), Ninf (Japan), DataFarm (Japan), N*Grid (Korea) . . .

■ Australia:Nimrod-G, Gridbus, GridSim, Virtual Lab, DISCWorld, GrangeNet . . .

■ EU: UNICORE, UK eScience, EU Data Grid, EGEE (Enabling Grids for e-Science (EU funded)), EuroGrid, CrossGrid GEMSS GRIA GridLab NextGridCrossGrid, GEMSS, GRIA, GridLab, NextGrid ...

■ USA: Globus, Legion, OGSA, Sun Grid Engine, Oracle, NASA IPG Condor-G NetSolve AccessGridNASA IPG, Condor-G, NetSolve, AccessGrid . . .

Architecture & Infrastructure

■ Service oriented architecture– Based on Web Services technology.

P ibl f t i t ti ith th h ti– Possible future integration with other hosting platforms via OGSA.

■ Client-side Framework– Plug-in component client framework.– Service Proxies

■ Service-Provison Framework (from VGE)■ Service Provison Framework (from VGE)– Generic Application Services

■ Secure Transport and Messaging (end-to-end security)Q S S t (f VGE)■ QoS Support (from VGE)

■ Accounting and Billing (Grid Economy)

Grid Middleware■ When using a PC or

workstation you– Login with a username

■ When using a Grid you– Login with digital

credentials – single sign-gand password (“Authentication”)

– Use rights given to you (“A th i ti ”)

credentials single signon (“Authentication”)

– Use rights given you (“Authorisation”)(“Authorisation”)

– Run jobs– Manage files: create

th d/ it li t

( Authorisation )– Run jobs– Manage files: create

them read/write listthem, read/write, list directories

■ Components are linked by a bus

them, read/write, list directories

■ Services are linked by the Internetbus

■ Operating system ■ One admin. domain

Internet■ Middleware■ Many admin. domains

Grid Computing -p gApplications

■ On-demand, real-time computing: Medical instrumentation & Mission Critical.Di t ib t d HPC (S ti ) C tl i■ Distributed HPC (Supercomputing): Computl science.

■ High-Capacity/Throughput Computing: Large scale simulation/chip design & parameter studies.

■ Content Sharing: Sharing digital contents among peers.■ Remote software access/renting services: Application service

provides (ASPs) & Web services.■ Data-intensive computing: Drug Design, Particle Physics, Stock

Prediction...■ Collaborative Computing: Collaborative design, Data exploration.■ Service Oriented Computing (SOC): Towards economic-based Utility

Computing Web Services

Grid Applications

Grid Applications

■ Focus on Computational Grid Aspects

■ Compute intensive numerical methods (parallel MPI codes)■ Compute-intensive numerical methods (parallel MPI codes)– Finite Element Method (FEM)– Computational Fluid Dynamics

M t C l Si l ti– Monte Carlo Simulation– ML-EM Method

■ Data Transfers (few MBs to few GBs)—Medical Images! ( ) g■ Services composed of multiple application components ■ Flexibility – User Interactions■ Near-realtime requirements

NASA Information Power Grid (IPG)

■ Connects 6 NASA centers

■ Access to NASA resources■ Access to NASA resources

– supercomputers, instruments, – databases, people, ...

■ Multi-disciplinary simulations, remote steering, collaboration

■ IPG provides middleware for– discovery, scheduling,– deployment, security and policy – enforcement w.r.t. NASA resources.

http://www.ipg.nasa.go

InternetGEMSSFEM

GRID RAPT

FEMCFX

RAPT CFX

Client

FEM

RAPT

Program

FEM

CFX

Client

Client

Clientwww.par.univie.ac.at/~sigi/index.html#projects

EU Data Grid, EGEE (CERN)

Grid Landscape

USA Grid Landscape

Japan Grid LandscapeHokkaido UniversityHokkaido University

Inter-university Computer Centers(excl. National Labs) circa 2002

University of Tsukuba

Kyoto University

University of Tsukuba

Tohoku University

NEC SX-4/128H4(Soon SX-7)NEC TX7/AzusAKyushu

University of Tokyo

yUniversity

Nagoya UniversityOsaka University

Tokyo Inst. Technology (Titech)

National Research Grid Initiative (NAREGI)

■ A new R&D project funded by MEXT – FY2003-FY2007– ~2B JPY budget in FY2003– 1.5B for Grid R&D, 0.5B for Nano-tech apps.

■ One of Japanese Government’s Grid Computing Projects■ Selected National Labs Universities and Industry■ Selected National Labs. Universities and Industry are to be involved in the R&D activities■ Near-realtime requirements■ Near-realtime requirements

GEMSS: GRIDGRID--enabled Medical Simulation Servicesenabled Medical Simulation Serviceshttp://www gemss de

Grids for Medical Appshttp://www.gemss.deGrid middleware initiative within medical application setting.

SimulationGrid Software Bio-numeric Medical Legal /Imaging Software

/solutions modelling Expertiseg

Aspects

Consorti mConsortium:10 partners from industry & academia including University clinics;NEC Europe Ltd, MPI Leipzig, ISS Vienna, CFX Ltd, CRID FUNDP, IT Innovation, USFD, IDAC Ireland Ltd., ASD, IBMTP Vienna.

Nano-techInformaticsBio-

I f ti

MedicalInformatic

eBiz, service Solution

Grid enabled Engineering Grid enabled Business

HPC

Portal/ASP

ChemInfoma

Informatics ssWebservice

serviceserviceUtility

Community

Grid Technology

Portal/ASPAccess

Gridservice

Personal Wether service

Upper Middle

Operation/Human resoureOperation/Human resoureUbiquitous Grid

Community InfraScience and Engineeringplatform

Peta Scale Grid Ubiquitous Grid

GPSGPSsensor

E th QE th Q

Database/Expression

DistributedMega Computing1PFLOPS

Peta Scale Grid

Lower MIddle

High speed network (over 1G – 10G)

Wireless networkTremendous SensorHuge datastream

GPSGPS

CamCam MobileMobile

Earth QEarth QStorage10PB

1PFLOPSPC x 100MSC x 10000 P2P grid

Server tech

IPv6 SecurityBlood

pressureBlood

pressure

FTTH/xDSL/Wireless AP/100M-

Server tech

AIST Grid ProjectsF di

Project ParticipantsFunding

Agency, Period, Budget (2002/Total) Focus Accomplishment

Ninf AISTTITECH

U. TsukubaKyoto U

JST, METI, IPA 1994- $2.5M/6.0M

Develop Grid Middleware and application (Ninf-G /Grid RPC)

Ninf-G Release,Preliminary application development

Kyoto U. (Ninf G /Grid RPC)

ApGrid Grid partnership in Asia Pacific Region

MEXT, 2002-2004$0.3M / $0.9M

partnership for Grid computing in the Asia Pacific region. Resource sharing, Grid development, mutual support for applications development

Engineering guideline availableAnnual workshops and core meeting for engineering

Grid Data Farm AIST,TITEC,KEK,U.T k NEC

METI, 2002 – 2002 Construction of a Peta- to Exascalell l fil l i i l l

Involved in the ATLAS/CERN Project d h li i i f hGrid Data Farm -Tokyo,NEC $ 2.3M parallel filesystem exploiting local

storages of PCs spread over the world-wide Grid

and the preliminary version of the GFarm is available. Also, demonstrated at SC02

GRID Drug Design

AISTU. Tsukuba

Tokushima U.JST, 2001-03,

$0.4M / 1.2MDrug design system on grids

PrototypedDesign

AIST Grid AISTOsaka U.

AIST, 2002-04$1.5M / 4M

Access Grid, Medical application, tele science Prototyped

Quantum Ch i t G id

AIST AIST, $1M Provide user friend portals Open for limited users

Chemistry GridGrid Consortium

Japan

AIST and Business section

$0.1M Venues for info exchange, GGF franchise

36 companies, 150 memberships

Other related Grid Projects

project participants Contacts SupportAgency, Period,

Budget (2002/Total)

Focus Accomplishment

Bio Grid Osaka-u, pharmacy Industry, NEC, AIST,

MEXT 2002-2006 Bio Application deployment

Consortium organized

ITBL JAERI, RIKEN, NAL, http://www.itbl jp/

MEXT, 2001-2005

Application for supercomputer

1st phase donebl.jp/ 2005 supercomputer

ensemble

Super SINET

Universities http://www.nii.ac.jp/

MEXT, 2001-04unknown

10G backbone 1st phase done

SINET

NaReGI UniversityNational Labs

NA MEXT, 2003-07,FY02 4.5B

JPYFy03 2B JPY

National Research Grid Infrastructure

Inaugurated

Fy03 2B JPY (x5yrs)

BusinessGrid

IT vendorsAIST

NA METI, 2003-05Total 2.8B JPY x

3yrs

Business application

Approved/prepare for public call

Medical Data ConstraintsVery large databases of images with a very long term storageData distributed over acquisition sites Sensitive medical data (strong security constraints)Sensitive medical data (strong security constraints)Sensitive medical data (strong security constraints)Read-only access to raw scanner data (replicas needed))Keep track of data processingDifferent kinds of users (patients, physicians and researchers)researchers)

Needs We need a high performance and extensibility architecture that is designed for medical applications.We need an architecture that is appropriate for developingWe need an architecture that is appropriate for developing distributed medical systems.The concept of multilayered architecture to allow us to deal

ith th d i d d l t f l twith the design and development of a complex system as a distributed medical application is.Computing GRIDS are very useful for medical image t d l i (di t ib t d d t hi h istorage and analysis (distributed data, high processing

power).The system should allow the physicians to get secure

t th i d t d t i h b id taccess to their data and to issue hybrid requests over huge databases.

Goals■ To supply a Distributed System for the medical community

able to deal with medical images and sensitive medical metadatametadata

■ To provide hybrid content based queries and metadata queries over large databases of medical images (imagequeries over large databases of medical images (image processing)

■ To transparently use GRIDS in order to get advantage of its computing power and its massive storage capacity

■ To propose an architecture for developing this kind of s stems (DSE Distrib ted S stem Engines)systems (DSE: Distributed System Engines)

ChallengesContent-based queries over imagesImage processingImage processingInterfacing Grids and Image Archiving S stemsSystemsDistributed queriesMassive storageHigh performance computingHigh performance computing

ApplicationsHybrid queries to images' databases of Mammography and IRM cardiological sequences

Metadata– Metadata– Parametric analysis (indexing)– Content processingp g

Similarity queriesMost similar Less similar

Similaritymeasuresmeasures

Applications: Images

Applications: Images

Medical Applications

Simulation/Imaging

Grid Software /solutions

Bio-numeric modeling

Medical Expertise

Legal Aspects/Imaging

Software/solutions modeling Expertise Aspects

Medical ApplicationsN D i ClName Domain Class

Maxillo-facial surgery simulation

Medicine – pre-surgical planning

On demand / distributed supercomputingsurgery simulation planning supercomputing

Neurosurgery support

Medicine – intra-operative planning

On demand

R di th M di i M t C l O d d / di t ib t d Radiotherapy planning

Medicine – Monte Carlo treatment simulation

On demand / distributed supercomputing

Inhaled drud d li l i

Medicine – air flow d i

On demand / distributed idelivery planning dynamics supercomputing

Cardio-vascular system simulation

Medicine – blood flow dynamics

On demand

Advanced image reconstruction

Medicine – nuclear / in vivo diagnostics

On demand

Medical Applications“Grids will enable a standardized, distributed digital mammography

f i i

• Digital image archives• Collaborative virtual

“Grids make it possible to l ll i f

resource for improving diagnostic confidence"environments

• On-line clinical use large collections of images in new, dynamic ways, including medical diagnosis ”

conferences

diagnosis.”

“The ability to visualise 3D medical images is key to g ythe diagnosis of pathologies and pre-surgical planning”

Medical Applications

•Capturing the complex and evolving patterns of genetic information, determining the development of an embryo• Understanding the genetic interactions that underlie the processes of life form development

“Every time a new genome is

underlie the processes of life-form development, disease and evolution.

Every time a new genome is sequenced the result is compared in a variety of ways with other genomes. y gEach code is made of 3.5 billion pairs of chemicals…”

Enhanced Medical Image Reconstruction in Grid Reconstruction in Grid Environments

■ SPECT: tomographic imaging modality in Nuclear Medicine

■ Advanced 3D method (ML-EM): Improved resolution, enhanced contrast, differentiation of small lesions

■ Grid benefits:- iterative 3D reconstruction

is compute intensive - enables improved tumourenables improved tumour

diagnosis outside specialised medical centres

References

www.cern.ch/lcgwww.eu-egee.org

lhcathome.cern.ch

www.eu-egi.org

www.gridcafe.org

References

http://www.cs.utk.edu/~abukhzam/grid-tutorial.htmhttp://www.cs.wisc.edu/condorp // /http://www.globus.orghttp://icl.cs.utk.edu/netsolvehttp://nws cs ucsb edu/http://nws.cs.ucsb.edu/http://icl.cs.utk.edu/sinrg

Conclusion

Grid ComputingGrid InfrastructureGrid InfrastructureGrid ApplicationsGrid use in Medical ApplicationsGoals and Challenges Conclusion