Post on 04-Jan-2016
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