lydia maigne, phd laboratoire de physique corpusculaire [email protected]

INFSO-RI-508833

Enabling Grids for E-sciencE

Grid based telemedicine application for GATE Monte Carlo dosimetric studies using HOPE (Hospital Platform for E-health)

Lydia MAIGNE, PhDLaboratoire de Physique [email protected]: M.Diarena, S.Nowak, C. Thiam, D.Donnarieix and V.Breton

Lydia Maigne EGEE User Forum – Clermont-Ferrand - 11-14 February 2008 2


Better treatment requires better planning

• Today: analytic calculation to compute dose distributions in the tumor or to model scans

– For new Intensity Modulated Radiotherapy treatments, analytic calculations off by 10 to 20% near heterogeneities

– Quantification of the radioactivity inside an organ or a tumor after an internal dosimetry treatment very difficult to perform

• Better alternative: Monte Carlo (MC) simulations

unacceptable time consuming• The GRID impact: reduce MC computing time to a level acceptable

by the physician



GATE: Geant4 Application for Tomographic Emission

noyauGEANT4

Développementsdédiés

SPECT/TEP

GATE

Goal: Develop a platform dedicated to PET and SPECT applications

Ensure a long terme development

• Based on Geant4

• Dedicated tools for imaging

• Easy to use

• Shared developement• OpenGATE collaboration



Medical physics Application

Some examples of medical physics applications:

PET camera

Ocular brachytherapy treatment

Radiotherapy

OpenGATE collaboration

http://www.opengatecollaboration.org



Pseudorandom number sequences

Random numbers consumption depends of: interactions, number of particles générated, etc…Linear RN consumption in function of the number of particles generated

x15x14x13x12x11x10x9x8x7x6x5x4x3x2x1

status000.rndm status001.rndm status002.rndm

Status 1 Status 2 Status 3

1000 status of the Random Number Generator with a length of 3.1010 numbers.



6°) Split the simulations:

• A GATE simulation generating a lot of particles in matter could take a very long time to run on a single processor

– So, the big simulation generating 10M of particles is divided into little ones, for example 10 simulations generating 1M of particles 20 simulations generating 500000 particles 50 simulations generating 200000 particles ……

– All the other files needed to launch Monte Carlo simulations are automatically created with the program.

jdl filesjobXXX.jdl

Script filesscriptXXX.csh

Required filesMacro files

macroXXX.macStatus files

statusXXX.rndm



An ocular brachytherapy treatment

2°) Treatment planning

Calculation ofdeposit dose on the tumor

(~1mn):A treatment plan is

developed using the images

1°) Obtain scanner slices images

The head is imaged using a MRI and/or CT scanner

3°) Ocular brachytherapy treatment

Irradiation of the ocular tumor using an ophthalmic

applicator



HOPE portal



How it works on the grid

DICOM

DICOM

ConfidentialMetadata


DICOM

DICOM



AMGAAMGA

UI

PACSPACS

I mages DI COM

Cryptage HTTPS

GridFTP

Medical images access on the gridFTP like transfers: GridFTP secured protocol

Metadata access:Proxy authentication and VOMS authorizationSecured connections using SSL (Secure Socket Layer):

cryptographic systems to transfer documentsAuthorizationb using ACLs (Access Control List)

Medical images management on the grid (AMGA server):Langages based on SQLHierarchical organizationof metadata (tree)Dynamic schemas: add,delete, modif y fieldsReplication of metadata on other AMGA servers

AMGAAMGA

UI

PACSPACS

I mages DI COM

Cryptage HTTPS

GridFTP

Medical images access on the gridFTP like transfers: GridFTP secured protocol

Metadata access:Proxy authentication and VOMS authorizationSecured connections using SSL (Secure Socket Layer):

cryptographic systems to transfer documentsAuthorizationb using ACLs (Access Control List)

Medical images management on the grid (AMGA server):Langages based on SQLHierarchical organizationof metadata (tree)Dynamic schemas: add,delete, modif y fieldsReplication of metadata on other AMGA servers

•The telemedicine infrastructure allows users to exchange medical data from different hospitals. •The information exchange takes place between web services deployed in different locations and is completely transparent to end users. •Information are transferred between different locations only on request using SOAP messages over an SSL secured channel.

Security:



1°) The connexion: to a smart adaptive identifier

• Patient smart card– The card will host an identifier based on hash value of

name, birth and family components– Each medical act is stored as a small string in the card

• Encryption– Between the PACS server and the web portal, patient

ID and medical act will be a part of a key used to encrypt images to secure transactions

• Data retrieval system– A secure authentication for patients to retrieve their

personal data

• Physician authentication– Healthcare professional smart card + Password

Look at the poster of Paul de Vlieger:Smart adaptive identifier for a medical data retrievement system



1°) Enter a new patient: patient sheet



2°) Obtain the medical images of the tumor from the PACS system

Scanner slices:DICOM

or DICOMRT format

• Scanner slices of the patient brain in DICOM or DICOMRT format

Size of the scan slices: 512 X 512 X 1 pixels

Size of a voxel in the image: 0,625 X 0,625 X 1,25 mm



2°) Obtain the medical images of the tumor from the PACS system



3°) Register and replicate the binary image on SEs:

StorageStorageElementElementComputiComputi

ngngElementElement


ngngElementElement


ngngElementElement


ngngElementElement

Image:text file

Binary file:Image.rawSize 19M

Scanner slices:DICOM format

An

on

ym

isatio

n

Concatenation

DICOM Viewer

Site 1

Site 2

Site 3

Site 4

User interfaceUser interface

Copy image.raw

on a SE

Replicate image on the

other SEs



4°) Register the confidential data:

Image:text file



AMGADatabase

An

on

ym

isatio

n

Concatenation


ngngElementElement


ngngElementElement


ngngElementElement


ngngElementElement

Site1

Site2

Site3

Site4

Register lfn in the

database

Query a lfn to the

database




5°) Submission on the Grid:


ngngElementElement


ngngElementElement


ngngElementElement


ngngElementElement

GATE

GATE

GATE

GATE

Image:text file



AMGADatabase


Site1

Site2

Site3

Site4

An

on

ym

isatio

n

Concatenation

Submission of jdls to the

CEs

Copy the medical image from the SE to

the CE

Retrieving of root output

files from CEs



5°) Submission on the Grid:



6°) Results: Treatment plans



Computing time tests

Local computing:260 hours on a 3GHz processorGrid computing: 10 hours for 50 divisions

Gain in computing:

26

Radiotherapy

Local computing:907 days on a 3GHz processorGrid computing:3 days for 1813 jobs, 24h/ job

Gain in computing

800

SPECT

Local computing:260 hours on a 3GHz processorGrid computing: 10 hours for 50 divisions

Gain in computing:

26

Radiotherapy

Local computing:907 days on a 3GHz processorGrid computing:3 days for 1813 jobs, 24h/ job

Gain in computing

800

SPECT



Conclusion and perspectives

• New convivial web platforms for different biomedical applications

• Tools begin to be used by medical staff

• Grid stability, EGEEIII infrastructure• Develop convivial end users tools to be used in

hospital• Federate the communities (the HealthGrid association)• Confidential data, legal issues• Think about the electronic medical patient file and how

the grid could deal with in a near future.



References

• EGEEII biomedical applications:– http://egee-na4.ct.infn.it/biomed/egee2-applications.html

• WISDOM: – www.wisdom.healthgrid.org

• LifeGrid project:– www.lifegrid.org

• GATE Monte Carlo platform:– www.opengatecollaboration.org

• HealthGrid initiative:– www.healthgrid.org

• Contacts:– PCSV team web site: http://clrwww.in2p3.fr/PCSV– Lydia Maigne: [email protected]

lydia maigne, phd laboratoire de physique corpusculaire [email protected]

Documents

medical images

ocular tumor

particles20 simulations

example10 simulations

particles50 simulations

monte carlo simulations

new patient

better treatment