1. Daniel Pérez-Astudillo World Congress 2009, Munich, 12 September 2009
Optimization of an external beam radiotherapy treatment using GAMOS/Geant4
Pedro Arce
Juan Ignacio Lagares
Daniel Pérez-Astudillo
(CIEMAT, Madrid)
John Apostolakis
Gabriele Cosmo
(CERN, Geneva)
World Congress 2009, Munich, 12 September 2009

2. Outline 1. GAMOS radiotherapy framework 3. GAMOS usage Summary
RT simulation in Geant4
GAMOS RT: geometry
GAMOS RT: source
GAMOS RT: scoring
GAMOS RT: phase space files
GAMOS RT: dose in phantoms
3. GAMOS usage
Documentation
Tutorials
Installation
Summary
Future prospects
2. OPTIMISATION
Automatic optim. physics cuts
Geant4 EM physics parameters
Fast navigation in phantoms
CPU time comparison BEAMnrc / DOSXYZnrc
CPU time reports
Particle spliting

3. GAMOS Radiotherapy framework

4. Radiotherapy simulation with GEANT4
GEANT4 is a very powerful and flexible toolkit
Outstanding geometry and visualisation
Well proven physics
Big flexibility
Many thousands of users…
But it is not easy for a beginner to simulate a radiotherapy treatment
Everything requires writing C++, and a good knowledge of GEANT4 details
Very few tools specific for radiotherapy
No optimisation specific for radiotherapy
GAMOS provides a Geant4-based framework easy to use and flexible
EASY: With a few simple commands you can simulate a full radiotherapy treatment using a text file
FLEXIBLE: Based on plug-in technology.
No predefined components, they are selected ‘on the fly’, by the user commands
If you have a new requirement not covered by GAMOS, it is very easy to plug your code into the GAMOS framework and select your code with a user command
+ several tools have been developed to optimise a radiotherapy application

5. GAMOS Radiotherapy framework
Geometry
Any accelerator geometry can be built with a simple geometry format
Based on solids (box, tube, polycone, ..) + their union/substraction/intersection
Any material can be used (+400 predefined for user convenience)
Several modules to simplify complicated elements (jaws, applicators, MLCs)
// PRIMARY COLLIMATOR
:P PC_ZMIN 1.6*cm
:P PC_ZMAX 7.6*cm
:VOLU “primary collimator" TUBE 0 10*cm ($PC_ZMAX-$PC_ZMIN)/2. G4_W
:VOLU "primary collimator_hole" CONE
($PC_ZMAX-$PC_ZMIN)/2. G4_AIR // continuation line
:PLACE "primary collimator" 1 expHall RM $PC_ZMIN+$PC_ZMAX)/2.
:PLACE "primary collimator_hole" 1 "primary collimator" RM
:COLOR "primary collimator" // RGB color
Movements:
Any volume can be moved with a user command

6. GAMOS Radiotherapy framework Source (primary generator)
Flexible source generator:
One or several particles of any type: photon, electron, positron, proton, neutron, isotopes, ...
Each one can be assigned a distribution of position, direction, energy and time
The usual medical physics distributions are available
Users can easily add a new one (plug-in’s)
/gamos/generator/directionDist my_source
GmGenerPositionDiscGaussian 1*mm
/gamos/generator/timeDist my_source GmGenerTimeDecay

7. GAMOS Radiotherapy framework
Scoring
Scoring is an important part of your simulation  powerful and flexible framework:
Many possible quantities can be scored in one or several volumes
● Dose ● Deposited energy ● Flux (in/out/passage)
● Current (in/out/passage) ● Charge ● Step length
● Number of particles ● Number of interactions ● Number of 2ary particles
● Number of steps ● Minimum kinetic energy ● …
For each scored quantity one or several filters can be used (almost 100 available)
Only electrons, only particles in a certain energy range, …
Several ways to classify the different scores
One different score for each different volume name, or volume copy, or energy bin, …
Results can be printed in one or several formats for each scored quantity
Standard output, text file, binary file, histograms
All scored quantities can be calculated with/without errors
All scored quantities can be calculated per event or per job
Taking into account correlations from particles from same event
Everything managed with user commands

8. GAMOS Radiotherapy framework
Phase space files
Write phase space files at one or several Z planes
Energy, position, direction
Save extra info:
Regions particle traversed
Regions particle created
Regions particle interacted
Particle origin Z
BIG FLEXIBILITY
More can be easily added (they are plug-in’s)
User selects which infos and how many bytes each one occupies
Use phase space files as generator
Displace or rotate phase space particles
Reuse phase space particles
Optional automatic calculation of reuse number
Optional mirroring in X, Y or XY
Recycle phase space files
Command for skipping of first N events
Command for optional histograms of particles read
Filter particles by extra info
Also read EGSnrc phase space format
VRML view of VARIAN 6000 accelerator

9. GAMOS Radiotherapy framework Dose scoring in phantoms
Simple voxelised phantoms ones with a few user commands
Read DICOM files
Read GEANT4 format (example advanced/medical/DICOM)
Read EGS format
Several outputs
standard output
text file
binary file
histograms
Displace or rotate read-in phantom geometry
PTV/CTV/GTV management
Many filters to write several dose files
in the same job
We have developed a tool that allows to insert objects in phantom geometries and produce the interactions in them
Realistic simulation of brachytherapy sources or ionisation chambers inside phantoms!
gMocren visualisation of DICOM geometry and tracks

10. Dose visualisation
IMRT dose visualisation with CIEMAT’s tool (MIRAS)
gMocren view of GEANT4 geometry and tracks transported with the new algorithm
gMocren view of GEANT4 geometry and tracks transported with the new algorithm
gMocren view of GEANT4 geometry and tracks transported with the new algorithm
gMocren view of GEANT4 geometry and tracks transported with the new algorithm
gMocren view of GEANT4 geometry and tracks transported with the new algorithm

11. Optimisation:
physics cuts

12. Optimisation: physics cuts
Two kind of physics cuts in Geant4:
Production cuts
Limit secondary production for ionisation and bremsstrahlung
User Limits:
Limit step size: better done tuning EM physics parameters
Limit track length: not relevant in accelerator simulation
Limit time of flight: not relevant in accelerator simulation
Kinetic energy/range minimum (stop particle if kinE/range below a limit): useful to kill particles when energy not enough to reach target
Better use range as more uniform for different regions
Better use kinetic energy as more efficient calculation
GAMOS solution: use kinetic energy inside but user provides it as range through a command
Different physics cuts per particle and region (group of volumes) can be defined through user commands

13. Optimisation: setting best cuts
Normally people want that optimising the cuts produces only few % (or less) difference in results
need to run very big statistics
Cuts can be very different for different particle/regions, and a cut in one particle/region may affect another
need to run many sets of cut values
RESULT: usually people think it is too complicated
Do not optimise cuts
Just copy the cuts from someone, which usually are not the best one for them
In GAMOS we propose an
Automatic determination of best production cuts or user limits in one job

14. Accelerator: Automatic optim. prod cuts
Which is the highest cuts we can use without decreasing the number of particles reaching the phase space plane?
In GAMOS we use an ‘inverse reasoning’:
For each particle reaching the plane
Get range of particle when it was created
Get range of mother particle where it was created
Do it consecutively for all ancestors
Make statistics of all these ranges, per particle, region and process
At EndOfRun print statistics of which is the minimum range per particle, region and process
You know that if you use a bigger production cut at least one particle would be killed and this particle or one of its children would not reach the phase space

15. Accelerator: Automatic optim. prod cuts
Often you allow some small % loss of particles..
Make histograms of all ranges per particle, region and proces
Provide an script so that for a given cut it gives you the proportion of particles killed
Be aware of double counting (very low: < 1%)
Make histogram of minimum range
(one entry per particle + particle ancestors) and
use it for statistics of % particles killed
Check distributions of killed particles
How to use it in GAMOS:
Add one user command in one job
/gamos/userAction GmProdCutsStudy RTPhaseSpaceFilter
Similar approach for user limits…
log10(range) of gammas created at target that reach the patient or produce a secondary that reaches the patient
CUT

16. Dose: Automatic optim. prod cuts
In GAMOS we use an ‘inverse reasoning’:
Compute dose due to particles that would be killed with a certain cut (and all their daughters)
When a particle is killed dose is deposited locally  only count dose in voxels different that where particle is killed by cut
GAMOS can estimate in one job the dose with several different cut values
Use GAMOS filters
/gamos/filter ProdCutFilter GmProdCutOutsideVoxelFilter 10.*mm 1.*mm
/gamos/scoring/addFilter2Scorer ProdCutFilter PDDscorerPC10.1.
It may happen that dose is small but distributed in a different manner than total dose (produces bias)
Use GAMOS dose histograms
to check dose shape
Similar approach for user limits…
Percent Depth Dose curve for all particles and particles rejected by cut 1mm/e- 10 mm/gamma

17. RESULTS: Optim. production cuts
We have simulated a VARIAN accelerator
Plane at 100 cm from the source and of halfwidth 100 cm in X and Y.
We have computed the dose in a water phantom of 104 voxel
Select the maximum cut values that change the total dose << 1 %
A command serves to apply production cuts to other processes, not only ionisation & bremsstrahlung (not needed but useful)
We gain an extra 3% in CPU time
RANGE REJECTION:
Automatic procedure gives you also information on number of particles that would be killed by range rejection
Does not improve CPU significatively (few particles are killed by it)
CPU gain
Accelerator: 20% w.r..t cuts ‘standard’ in BEAMnrc/DOSXYZnrc (700 keV for e-/e+, 10 keV for gamma)
Dose: 0-30% w.r..t cuts ‘standard’ in BEAMnrc/DOSXYZnrc

18. electromagnetic physics parameters
Optimisation:
Geant4
electromagnetic physics parameters

19. Select EM physics parameters
In Geant4 there is a long list of parameters that a user can change...
METHOD:
Start with default values which have been found good for radiotherapy simulations
They are quite conservative
Except dE/dx and lambda tables bin size
Change values to less precision and watch CPU time
Only consider those for which CPU gain is not neglibible
Try also higher precision to check
Watch for change in dose (look also at change in phase space)
OUTCOME:
Increase number of bins in dE/dx and lambda tables
Switch off energy loss fluctuations
Parameters that change charged particles step size vs precision
Option 1: msc range factor = 0.05
Option2: Rover range = 0.8
Lambda factor = 0.2
Msc step limitation = Minimal
Detailed study can be found at

20. Number of bins dE/dx and lambda tables
Number of bins of dE/dx and lambda tables: 120  1200
No change in Emax (for 10 MeV 500 bins would have same effect)
PDD: new/old
Phase space change:
About 1 % less particles, uniformly distributed in E and space
Dose change:
0.6 – 0.8 % decrease in dose, increasing with energy
CPU gain: -1 % default cuts
0% optimised cuts
CONCLUSIONS: we recommend using new values

21. No energy loss fluctuations
Phase space:
Reduces substantially number of low energy (< 0.2 MeV particles)
Dose:
Increases dose by % quite uniformly
CPU gain: 13 % default cuts
1 % optimised cuts
CONCLUSIONS: acceptable, but not really needed
phase space E: new/old
PDD: new/old

22. Fewer e-/e+ steps option 1
Phase space:
About 1.5 % more particles, uniformly distributed in E and space
Dose:
1.3 – 1.4 % increase in dose, quite uniformly
CPU gain: 29 % default cuts
12 % optimised cuts
CONCLUSIONS: acceptable, with caution
(if change in dose is fully uniform it does not matter)
PDD: new/old

23. Fewer e-/e+ steps option 2
Phase space:
About 1.8 % more particles, uniformly distributed in E and space
Dose:
~2 % increase in dose, bigger at low Z
CPU gain: 47 % default cuts
20 % optimised cuts
CONCLUSIONS: not acceptable, too big change, not uniform
PDD: new/old

24. Optimisation:
Fast dose calculation in
phantom voxels

25. Fast voxel navigation algorithm
Indexes voxels and voxel materials
Takes profit of regular geometry to quickly locate distance to next voxel and next voxel ID
Voxel frontiers are skipped ‘on the fly’ if voxels share same material
Distributes dose along voxels traversed taking into account correcctions in energy loss and multiple scattering due to energy loss along path
4-6 times faster than other Geant4 algorithm
- Depends on number of voxels and number of materials
CPU Time spent on transporting 1000 PARTICLES in 4.5 million-voxel phantom using diffferent Geant4 algorithms
Old
Vx 3D
Nested
Reg.(4 mate.)
Reg.(57 mate.)
Reg.(500 mate.)
Reg.(No skip)
2030
1.98
1.62
0.30
0.40
0.74
1.45

26. CPU time comparison with BEAMnrc/DOSXYZnrc
Optimisation:
CPU time comparison with BEAMnrc/DOSXYZnrc

27. CPU time comparison with
BEAMnrc
VARIAN 2100 gamma accelerator:
106 events on Pentium Dual-Core 3 GHz
BEAMnrc
GAMOS/GEANT4
(auto. optim cuts)
6 MeV
277 s
359 s
248 s
18 MeV*
1020 s
897 s
599 s
* Same parameters as for 6MeV

28. CPU time comparison with
DOSZXYnrc
Dose in 104 5x5x3 mm water phantom: 106 events on Pentium Dual-Core 3 GHz
Dose in patient, 23 materials: 106 events on Pentium Dual-Core 3 GHz
DOSXYZnrc
GAMOS/GEANT4
(auto. optim cuts)
water
234 s
215 s
167 s
patient
300 s
170 s
166 s
Dependency with number of materials
DOSXYZnrc:
Only 4 densities: 297 s
All densities: 300 s
GEANT4:
4 materials: 166 s
23 materials: 274 s
68 materials: 360 s
196 materials: 454 s
Need to implement density-changing materials in Geant4…

29. Optimisation: time reports
User commands to get CPU time report
By particle, energy bins, volume, region (or combination of them)
Just add a user command, for example:
/gamos/userAction GmTimeStudyUA GmClassifierByParticle GmClassifierByEnergy
gamma/ : User=0.01 Real=0 Sys=0
gamma/ : User=2.01 Real=2.45 Sys=0.27
gamma/0.1-1: User= Real= Sys=1.51
gamma/1-10: User=4.25 Real=5.4 Sys=0.3
e-/ : User=0.07 Real=0.1 Sys=0
e-/ : User=0.54 Real=0.69 Sys=0.06
e-/ : User=4.71 Real=5.41 Sys=0.38
e-/0.1-1: User= Real= Sys=1.79
e-/1-10: User= Real= Sys=7.45
Example to get detailed gprof profiling about where (in which methods) the time is spent
Time spent in a method and integrated time in all the methods called by it

30. Optimisation:
Particle splitting

31. Particle splitting UNIFORM BREMSSTRAHLUNG SPLITTING
All bremmstrahlung photons are replicated the same number of times
Z-PLANE DIRECTION BREMSSTRAHLUNG SPLITTING
User defines a Z plane with limits in X & Y (represents entrance of phantom)
Same as uniform BS, but if gamma does not aim at Z plane, Russian roulette is played
EQUAL-WEIGHT SPLITTING
Similar as Z-plane direction BS, but splits every gamma produced, not only from bremsstrahlung
Russian roulette is played with e-/e+, so that very few reach Z plane
Aim is that all particles that reach phantom have the same weight
Based on EGSnrc DBS technique

32. Particle splitting: RESULTS
UNIFORM BREMSSTRAHLUNG SPLITTING
Maximum efficiency gain:
Z-PLANE DIRECTION BREMSSTRAHLUNG SPLITTING
Maximum efficiency gain:
EQUAL-WEIGHT SPLITTING
Maximum efficiency gain: 45
EGSnrc results with same accelerator: gain 80
Algorithm in GAMOS is not fully implemented yet…

33. Documentation, installation,
usage

34. Documentation A simple one and a few more complicated ones
User’s Guide:
Installation
All available functionality
How to provide new functionality by creating a plug-in
Examples:
A simple one and a few more complicated ones
/gamos/setParam GmGeometryFromText:FileName mygeom.txt
/gamos/geometry GmGeometryFromText
/gamos/physics GmEMPhysics
/gamos/generator GmGenerator
/run/initialize
/gamos/generator/addSingleParticleSource my_source gamma 6.*MeV
/run/beamOn 1000

35. Tutorials Four tutorials Radiotherapy tutorial PET tutorial
Histograms and scorers tutorial
plug-in tutorial
Propose about 15 exercises each
Explained in detail
Increasing in difficulty
Reference output provided
Solutions provided
4 GAMOS tutorial courses have been given in Europe and America
About 70 attendees
Next tutorial in European School of Medical Physics (October-November 2009)

36. Installation GAMOS is freely available from CIEMAT web
User registers and downloads installation scripts
We provide a no-choice but very easy way: one-line installation
sh installGamos.csh/sh $HOME/gamos
No need to manually download and install packages
No need to define enviromental variables
Checks that your system has the needed components
Downloads, installs and compiles CLHEP, Geant4, (optionally) ROOT and GAMOS in one directory
GAMOS compiles = Geant4
Optionally an expert user can make several choices
Current installation tested on Scientific Linux, Fedora Core, Debian and Ubuntu, and on MacOS
> 50 tests are run to check each new release

37. Summary
And
Future prospects

38. Summary
We have implemented in GAMOS many user commands & analysis tools to make easy an radiotherapy accelerator simulation
many GAMOS tools are already being used for other fields (PET, brachytherapy, microdosimetry, damage in electronics, …)
We have developed several tools to optimise the CPU time of an radiotherapy simulation
Automatically optimise production cuts and user limits
Optimisation of Geant4 electromagnetic physics parameters
Do detailed time studies with a user command
Algorithm for fast navigation in voxelised phantoms
Particle splitting techniques for acceleartor simulation
We improve CPU time by a factor of 100 w.r.t. ‘bare’ Geant4
We get similar CPU times as BEAMnrc / DOSXYZnrc

39. Everybody is invited to contribute!
Future prospects
Improve particle splitting (factor ~ 2 - 3)
Implement history repetition (factor ~ 2 - 5)
Fast Monte Carlo (factor ~ )
Provide standard and fast Monte Carlo in the same framework
Thanks to Geant4 flexibility
Everybody is invited to contribute!