GATE

Overview and recent advances

Nicolas KarakatsanisIrène Buvat

National Technical University of Athens, GreeceLaboratory of Functional Imaging, U678 INSERM, Paris, France

Outline

• Evolution of the use of MC simulations in ET since 1996

• OpenGATE motivation and short history

• New features in MC simulators in ET

• New applications for MC simulations

• Upcoming developments in MC simulations

• Conclusion

Evolution of the codes used for MC simulations in ET since 1996

1996-2000

• 14 different codes: - 10 « home-made » - 4 publicly released or available from authors

2001-2005

• 15 different codes: - 8 « home-made » - 7 publicly released or available from authors

No « standard » code for Monte Carlo simulations in SPECT and PET

SimSET

SIMIND

Most frequently used And recently

Penelope

GATE

Motivation for developing GATE in 2001

• Provide a public code- based on a standard code to ensure reliability- enabling SPECT and PET simulations (possibly even more)- accommodating almost any detector design (including prototypes)- modeling time-dependent processes- user-friendly

• Developed as a collaborative effort

The OpenGATE collaboration

From 4 to 23 labs worldwide

• U601 Inserm, Nantes• U650 Inserm, Brest• U678 Inserm, Paris

• LPC CNRS, Clermont Ferrand• IReS CNRS, Strasbourg• UMR5515 CNRS, CREATIS, Lyon,• CPPM CNRS, Marseilles• Subatech, CNRS, Nantes

• SHFJ CEA, Orsay• DAPNIA CEA, Saclay

• Joseph Fourier University, Grenoble

• Delft University of Technology, Delft, The Netherlands• Ecole Polytechnique Fédérale de Lausanne, Switzerland• Forschungszentrum Juelich, Germany• Ghent University, Belgium• National Technical University of Athens, Greece• Vrije Universiteit Brussel, Belgium

• John Hopkins University, Baltimore, USA• Memorial Sloan-Kettering Cancer Center, New York, USA• University of California, Los Angeles, USA• University of Massachusetts Medical School, Worcester, USA• University of Santiago of Chile, Chile• Sungkyunkwan University School of Medicine, Seoul, Korea

Product of OpenGATE: GATE

• Publicly released on May 2004 http://www.opengatecollaboration.org

• An official publication:Jan S, Santin G, Strul D, Staelens S, Assié K, Autret D, Avner D, Barbier R, Bardiès M, Bloomfield PM, Brasse D, Breton V, Bruyndonckx P, Buvat I, Chatziioannou AF, Choi Y, Chung YH, Comtat C, Donnarieix D, Ferrer L, Glick SJ, Groiselle CJ, Guez D, Honore PF, Kerhoas-Cavata S, Kirov AS, Kohli V, Koole M, Krieguer M, van der Laan DJ, Lamare F, Largeron G, Lartizien C, Lazaro D, Maas MC, Maigne L, Mayet F, Melot F, Merheb C, Pennacchio E, Perez J, Pietrzyk U, Rannou FR, Rey M, Schaart D, Schmidtlein CR, Simon L, Song TY, Vieira JM, Visvikis D, Van de Walle R, Wiers E, Morel C. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol 49: 4543-4561, 2004.

• More than 800 subscribers to the Gate users mailing list

Tasks of the OpenGATE collaboration

• Upgrade GATE for following GEANT4 new releases (1 major release per year)

• Incorporate new developments in GATE (1 minor release per year) e.g.:

variance reduction techniques (to be released soon)

speed-up options (e.g., analytical modeling of the collimator response in SPECT) (to be released soon)

tools for running GATE on a cluster or on a grid environment (to be released soon)

extension of GATE for dosimetry applications

tools for interfacing GATE output with other software (STIR)

• Organize training

GATE today: technical features

• Based on GEANT 4

• Written in C++

• User-friendly: simulations can be designed and controlled using macros, without any knowledge in C++

• Appropriate for SPECT and PET simulations

• Flexible enough to model almost any detector design, including prototypes

• Explicit modeling of time (hence detector motion, patient motion, radioactive decay, dead time, time of flight, tracer kinetics)

• Can handle voxelized and analytical phantoms

GATE today: practical features

• Can be freely downloaded, including the source codes

• Can be run on many platforms (Linux, Unix, MacOs)

• On-line documentation, including FAQ and archives of all questions (and often answers) about GATE that have been asked so far

• Help about the use of GATE can be obtained through the gate-user mailing list

• Many commercial tomographs and prototypes have already been modeled (including validation of the model)

www.opengatecollaboration.org

PET systems already modeled by the OpenGATE collaboration

Some examples

GE Advance/Discovery LS PET scannerSchmidtlein et al, Med Phys 2006

GE Advance/Discovery ST PET, 3D modeSchmidtlein et al. MSKCC

Some examples

Guez et al, DAPNIA and SHJF

D.Guez et al., HRRT, CEA/DAPNIA and SHJF

HRRT

SPECT systems already modeled by the OpenGATE collaboration

Example

Schielding

Backcompartment

NaI(Tl)crystal

Scanning table

Phantom holder

MEHR collimator

DST Xli camera

Assié et al, Phys Med Biol 2005

Some examples

Real data

Simulated dataC

ount

s (A

U)

0

1

2

3

4

5

6

7

50 70 90 110 130 150 170 190 210 230 250 270

Energy (keV)

FW

HM

(m

m)

3

5

7

9

11

13

15

0 5 10 15 20 25

source - collimator distance (cm)Assié et al, Phys Med Biol 2005

Energy (keV)

Cou

nts

(AU

)

0

2

4

6

8

10

12

14

50 70 90 110 130 150 170 190 210 230 250 270

Indium 111 source in air Indium 111 source in water

Prototypes already modeled by the OpenGATE collaboration

Example

PSPMT

crystal

collimator

Experiment GATE Energy (keV)N

um

be

r o

f co

un

ts

GATEExperiment

Energy spectrum

Lazaro et al, Phys Med Biol 2004

IASA CsI(Tl) gamma camera

…

Monte Carlo simulations today: what is new?

Modeling time dependent processes

SPECT and PET intrinsically involves time:

• Change of tracer distribution over time (tracer biokinetic)• Detector motions during acquisition• Patient motion• Radioactive decay• Dead time of the detector• Time-of-flight PET

GEANT 4 (hence GATE) is perfect in that regard

Modeling of radioactive decay

Santin et al, IEEE Trans Nucl Sci 2003

15O (2 min)11C (20 min)

Modeling of time of flight PET

Groiselle et al, IEEE MIC Conf Rec 2004

Type of studyType of study DetectorDetector Energy Energy Resolution Resolution (FWHM)(FWHM)

Low Energy Low Energy Threshold Threshold (keV)(keV)

Coincidence Coincidence Time Window Time Window (ns)(ns)

Non-TOFNon-TOF GSOGSO 15%15% 410410 88

TOFTOF LaBrLaBr33 6.7%6.7% 470470 66

3D ML-EM 3D ML-EM

Timing Timing resolutionresolution

NO TOFNO TOF

3ns3ns

TOFTOF

700ps700ps

TOFTOF

500ps500ps

TOFTOF

300ps300ps

Realistic phantoms can be easily used as GATE input

NCAT

Modeling realistic phantoms

Segars et al, Mol Imaging Biol 2004

MOBY

Segars et al, IEEE TNS 2001

Descourt et al, IEEE MIC Conf Records 2006

Modeling original detector designs

Non-conventional geometries

TEP/CT BIOGRAPH Siemens

Spherical geometry of the Hi-Rez PET scannerMichel et al, IEEE Conf Records 2006

lead end-

shielding

detection block

GEANT 4 is a very flexible tool

Modeling original detector designs

TEP/CT BIOGRAPH Siemens

Sakellios et al, IEEE MIC Conf Records 2006

Small animal imaging

Mouse-size gamma camera2 Hamamatsu H8500 PSPMT

NaI pixelized scintillatorTungsten collimator

Mouse-size PET1 Hamamatsu H8500 PSPMT

pixelated LYSO scintillator (30 x 35 crystals per block, 1 head = 1 block)

Modeling original detector designs

Sakellios et al, IEEE MIC Conf Records 2006

Small animal imaging

Simulated mouse gamma camera image

(MOBY phantom)

Simulated mouse PET image(MOBY phantom)

New applications for Monte Carlo simulations

Design and assessment of correction and reconstruction methods

Study of an imaging system response

Use in the very imaging process

Data production for evaluation purpose

Description and validation of a code

1995-1999 2000-2004

Optimizing detector design

Michel et al IEEE MIC Conf Records 2006

NEC curves as a function of the crystal in the PET HiRez scanner

Using Monte Carlo simulations for calculating the system matrix

“Object” f

Projection p

p = R fj

i

R(i,j): probability that a photon (or positron) emitted in voxel j be detected in pixel i

Calculating R using Monte Carlo simulations:• for non conventional imaging design (small animal)• to account for fully 3D and patient-specific phenomena difficult to model analytically (mostly scatter)

e.g., Lazaro et al Phys Med Biol 2005, Rafecas et al IEEE TNS 2004, Rannou et al IEEE MIC Conf Rec 2004

GATE is very appropriate but slow

Example: SPECT Tc99m phantom

Simulated data

22 cm21.2 cm

Real data

El Bitar et al IEEE MIC Conf Records 2006

What next?

Bridging the gap between MC modeling in imaging and dosimetry

Dewaraja et al, J Nucl Med 2005

SIMIND

DPM

DPM

GATE

GATE

GATE

The validity of the physics at low energy will have to be checkedProblems in G4 have been identified, e.g., multiple scattering and corresponding

energy deposit calculation

Modeling hybrid machines (PET/CT, SPECT/CT, OPET)

Integrating Monte Carlo modeling tools for:- common coordinate system- common object description- consistent sampling- convenient assessment of multimodality imaging

GATE

Brasse et al, IEEE MIC Conf Rec 2004

GATE

Alexandrakis et al, Phys Med Biol 2005

OPET

GATE

TOAST

PET/CT SPECT/CT

On-going studies regarding the use of GATE for CT simulations

Conclusion

• GATE is a very relevant tool for Monte Carlo simulations in ET

• Simulations will be more and more present in (nuclear) medical imaging in the future:

- as a invaluable guide for designing scanners, imaging protocols and interpreting SPECT and PET scans - in the very imaging process of a patient

Last but not least: next GATE training

• 3 days, March 7-9th, 2007 in Clermont-Ferrand, France

• GATE installation, GATE use through lectures and practical sessions given by GATE experts

• Registration will open on December, attendance will be limited

Registration from mid-december on http://www.opengatecollaboration.org

Acknowledgments

The OpenGATE collaboration

Thank you!!!Thank you!!!

Throughput of the simulations

The major problem with GATE and GEANT4!

•High throughput needed for efficient data production

•Large number of particles to be simulated- low detection efficiency- in SPECT, typically 1 / 10 000 is detected- in PET, 1 / 200 is detected

•Big “World”:- detectors have a “diameter” greater than 1 m- emitting object (e.g., patient) is large (50 cm up to 1.80 m)- emitting object is finely sampled (typically 1 mm x 1 mm x 1 mm cells)- voxelized objects are most often used

At least 4 approaches can be used to increase the throughput of the simulations

Using acceleration methods

•Fictitious cross-section (or delta scattering)•Variance reduction techniques such as importance sampling (e.g. in SimSET) speed-up factors between 2 and 15

Combining MC with non MC modeling

increase in efficiency > 100 Song et al, Phys Med Biol 2005

Full MC Collimator Angular Response Function

Parallel execution of the code on a distributed architecture

PET

old merger

new merger

Speed-up factor ~ number of jobs

De Beenhouwer et al, IEEE MIC Conf Records 2006

Smart sampling

Taschereau et al, Med Phys 2006