Creatis (insa lyon) J. Montagnat

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ACI GRID 2002 MEDIGRID: high performance medical image processing on a computational grid (accepted 3 years project)


  • J. Montagnat

  • LISI (INSA Lyon)

  • L. Brunie

  • ERIC (University Lyon 2)

  • S. Miguet


  • Medical image storage and processing on the grid

    • Structured data
    • Strong semantic (distributed medical files)
    • Sensitive (security issues)
  • Process complex algorithms with large computing power and memory requirements

    • parallel processing
  • Handle very large data sets

    • distributed storage
    • massive distributed processing
  • Context: datagrid testbed.

Scientific challenges

  • Shared and distributed data management

    • data hierarchy, dynamic indices, optimization, caching
  • Hybrid content-based and metadata queries

    • content-based queries, pre-processing, distributed queries
  • Complex modeling of anatomical structures

    • anatomical and functional models, parallelisation
  • Simulation of MRIs

    • MRI modeling, artifacts modeling, parallel simulation

Data and users

  • Medical Data

    • Images and metadata
    • Nominative (critical) and non-nominative (private) data
    • DICOM3 for medical images
  • Users

    • Patient: has free access to its medical data.
    • Physician: has complete read access to its patients data. Few persons have read/write access.
    • Researchers: may obtain read access to anonymous medical data for research purposes. Nominative data should be blanked before transmission to these users.

Data management requirements

  • Medical data requirements

    • Very large databases (Tb/year/hospital) and very long term storage (20 years for all images, up to 70 years in some cases)
    • Large amount of metadata
    • Access right on a medical department basis
    • Log data processing
  • Security related requirements

    • Data access security (Read-only access for most users, access right on a medical department basis, no read access to private data by any third party including system administrators)
    • Explicit control of sites where private data may be replicated
    • Nominative metadata should be stored in secure databases
    • Images should not be replicated outside hospital without blanking headers
    • Images content should be encrypted

Data infrastructure testbed

  • 4 image sources:

    • Acquired from Lyon cardiological hospital
    • Bone structure database from ESRF Grenoble
    • Mammographies from the DDSM
    • Simulated images

Foreseen medical data infrastructure

  • Split nominative and anonymous data to allow data replication on unsecured sites.


  • Image database importation

  • Hybrid content-based and metadata queries

    • Triggering parallel processing, distributed queries, DICOM server interface
    • pipeline processing
  • MRI simulation

    • parallel simulation kernel
  • Heart segmentation from MRI sequences

    • heart modeling, parallel job execution

Application example: Metadata query and image processing


  • year 1

    • setting up databases
    • statistical parameters computation
    • image indexing
    • spitfire-based security
    • MRI simulator
    • grid interface to DICOM servers
  • year 2

    • interface with datagrid / remote processing
    • distributed data management
    • spitfire extensions
    • content-based and metadata queries
    • pipeline processing


  • year 3

    • distributed data management
    • biomedical modeling
    • mammography studies
    • cardiac studies
    • first medical assessment

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