1. Un sistema avanzato per la gestione di immagini biomedicali in ambiente GRID
G. Aloisio, S. Fiore
SPACI Consortium & University of Salento, Italy, Lecce

2. Outline Scenario Related Works Issues Medical Imaging Environment
System Architecture
Data Storage
Workflow Management
Metadata Management: GRelC
Case Studies: CMCC & Climate-G
Security
Web Gateway
Conclusions
First what is Metadata? A common definition is that metadata is data about data. On the GRID, this is mainly information describing files that is necessary for running jobs, that is, file metadata. But
So, in a way accessing metadata is mainly about accessing databases. But having clients going directly to the database is not the most convenient solution. Better than that, is to have a simple interface for metadata access on the GRID. This interface should be defined in terms of metadata concepts, like entries, keys and values, instead of DB concepts. This has several advantages. It is easier to use by clients, since it exposes only metadata concepts and effectively hides the database.
Having a simple interface that reveals DB functionality solves most of the problems. Simplified relational database interface.

3. Scenario, issues and needs
Huge amount of medical data produced by several Centres (i.e. Hospitals)
Medical Interconnection Network: data sharing among several centres
Data integration, digital libraries, sharing are FP7 keywords
Need to move towards open, distributed and service-based environments
Patients
Challenging issues
Security
Data distribution
Data format heterogeneity
Metadata management
Metadata schema
Transparent access to the system
Scalable approach ….
Infrastructure
Clinicians 3

4. Grid for Biomedical Imaging, 1999
Parallel Rendering
Computational Grid
Globus
Pre Processing
Raw data
Server
HPC
Data
Acquisition
Collaborative working

5. Surrounding context
MediGrid
…and more…

6. Issues (high level) Medical image storage and processing
TAC, MRI, fMRI, PET, SPECT
DICOM image format
Privacy issues
Very large data sets handling
storage management (PACS)
Metadata management
Security of data
Process complex algorithms with large computing power and memory requirements
parallel processing
workflow Management System (WFMS)
TAC
MRI
fMRI
SPECT
PET

7. AGIR - gLite based

8. Medicus - Globus based

9. Computational Providers
Medical Imaging Environment
Info
Data Virtualization
Info
DICOM Images
DICOM Images
Computational Providers
DICOM Images
Cloud Environment
Info
Grid Environment

10. Main Issues (architectural level)
Full security support (data security/privacy)
Integration effort is needed to achieve high level results
Transparent management of data & metadata
Preservation of data locality (distribution of data)
Distribution of metadata
Access via Web Gateway
Workflow management for medical purposes
high level tools for end users
medical imaging methods composition
Grid support (storage, computation) on a large scale

11. Medical Imaging - Data Virtualization
Info
Data Virtualization
Info
DICOM Images
DICOM Images
Distributed Data & Metadata
Locality/Autonomy
Scalability
Legacy systems (PACS)
Security
Metadata
DB Encryption
Authorization/policies/roles
Data
Storage Encryption
Network
Communication protection
Interchange Protocols
DICOM Images
Info

12. Computational Providers
Medical Imaging - Analysis
Info
Data Virtualization
Info
DICOM Images
DICOM Images
Computational Providers
Analysis
Distributed
Grid Based
Cloud Based
Complex
Medical Workflow
Secure
Data anonymisation
Data Encryption
Authorization/Authentication
Cloud Environment
Grid Environment

13. System Architecture Security Web Gateway
Orchestrator/Collective Services
Grid Middleware
Metadata Service
Data Service
Comput. Service
MediGrid
Fabric Layer (data and metadata) 13

14. Medical imaging - Storage
Management of Medical Images
Secure on site management of DICOM images
Encryption of data
Anonymisation of data for analysis outside the centre
Access control through authorization
Storage interfaces between distributed environment and local storage devices
Secure and efficient data transport protocols
Backup of data ….

15. Biomedical Imaging Processing Issues
Huge amount of data
1 radiology department: 10TB/year;
1 CT dataset: 500MB-2GB.
Compute-intensive analysis
Registration noisy;
Difficulty in the segmentation;
Volume Reconstruction.
Need to explore several imaging analysis algorithms, validation methods, visualization pipelines
Grid Workflow Management System
Scheduler
Grid
Data
Mng CE
Node

16. Computational Providers
Medical Imaging - Workflow
Info
Info
DICOM Images
DICOM Images
Workflow Example
Denoising
Segmentation
Rendering
Cloud Environment
Computational Providers
Grid Environment

17. Metadata on the GRID Metadata is data about data
Metadata enables search and discovery
Metadata on the GRID
Mainly information about files
Usually stored on DBs
Need simple SOA & Grid interfaces for Metadata access
Advantages
Easier to use by clients
Robust paradigm
Exploiting Grid Security paradigm
Addressing Interoperability
First what is Metadata? A common definition is that metadata is data about data. On the GRID, this is mainly information describing files that is necessary for running jobs, that is, file metadata. But
So, in a way accessing metadata is mainly about accessing databases. But having clients going directly to the database is not the most convenient solution. Better than that, is to have a simple interface for metadata access on the GRID. This interface should be defined in terms of metadata concepts, like entries, keys and values, instead of DB concepts. This has several advantages. It is easier to use by clients, since it exposes only metadata concepts and effectively hides the database.
Having a simple interface that reveals DB functionality solves most of the problems. Simplified relational database interface.

18. Why are metadata so important?
Metadata is the key to manage, route and retrieve your data properly
PACS systems can be “federated” exploiting metadata
Search and discovery of “clinical case studies” can be afforded by metadata system
Metadata information increases the global knowledge about available patient data
From data to metadata and from metadata to knowledge to support Physicians for diagnostic and therapeutic purposes
How to manage “efficiently”, “securely” and transparently” metadata?
METADATA SERVICE

19. Metadata Service Metadata service
Compatibility with distrib. systems/security
Full security support for metadata
authorization (policies, roles, etc.)
authentication (based on grid certificates)
Access/Integration of metadata sources
Distributed vs centralized approach
Grid Access interface
Common interface for RDBMS and XML-DB
Database backend independence
GSI support

20. Metadata Management: Stack

21. GRelC Project (starting date 2001)
Grid Relational Catalog (GRelC) is a project which aims at designing and developing a set of efficient, secure and transparent Data Grid Services DB
XML

22. Grid Metadata Handling System: architecture in the small
GRelC DAIS

23. International Testbeds
Lecce (Italy)
Bejing (China)
GRelC Data Access
Data Sources (DB)
gandalf.unile.it
Linux x86
sara.unile.it
Mac OS X
sigma2.unile.it
Linux IA64
gridsurfer.unile.it
FreeBSD
galileo.hpcc.unical.it
sepac00.projects.cscs.ch
spacina.na.infn.it

24. Test Performance

25. CMCC: a fully distributed data grid environment
Grid Layer
VOMS Server
CMCC Data Grid Portal
Metadata Server (GRelC DAIS)
Grid Storage
GridFTP
GRelC Storage
CMCC CA
P2P layer
Data and Metadata resources
RDBMS (Oracle,MySQL, Postgres)
XML DBs (eXist, XIndice, etc.)
Search & Discovery
Two step process
Grid SS
Grid SS
Grid SS

26. A new research effort: Climate-G
The main goal of Climate-G is to create a unified environment for climate change, able to concentrate in the same context big amount of data geographically spread among several centres, rich metadata descriptions, efficient data access services, advanced data analysis and visualization tools, etc. exploiting and joining knowledge and skills in the fields of climate change and computational science 26

27. Climate-G partners
Università del Salento 27

28. Metadata Distribution and virtualization
For each site: Relational DB (index) XML DB (entire schema)
Virtualization/Integration layer: GRelC DAIS
Virtualization allows to conceal: Data distribution Number of sites,
RDBMS and XML back-ends P2P Topology Data Integration aspects technological details …

29. Security What about security? Authentication (mutual process)
Authorization
Access control lists
Role Membership Services
Data encryption (to ensure secure data communication)
Data anonymisation (to move data outside the Centers)
A certificate for each actor (user, service, host)
Each action is associated with an actor
Firewall protection …

30. Medical Web Gateway Main Functionalities Search & Discovery
Data access & viz
Workflow management
Users and roles mng ….
Features
Easy to use interfaces
Platform independent
Secured by design
Integrated environment
Data and …
Analysis tools
Visualization tools
Post-processing tools

31. Computational Providers
Medical Imaging Environment
Info
DICOM Images
Computational Providers
Cloud Environemnt
Grid Environment
Data Virtualization

32. Questions?
First what is Metadata? A common definition is that metadata is data about data. On the GRID, this is mainly information describing files that is necessary for running jobs, that is, file metadata. But
So, in a way accessing metadata is mainly about accessing databases. But having clients going directly to the database is not the most convenient solution. Better than that, is to have a simple interface for metadata access on the GRID. This interface should be defined in terms of metadata concepts, like entries, keys and values, instead of DB concepts. This has several advantages. It is easier to use by clients, since it exposes only metadata concepts and effectively hides the database.
Having a simple interface that reveals DB functionality solves most of the problems. Simplified relational database interface.