GAMOS radiotherapy optimisation WC2009, Sept. 12th 2009 1 Optimization of an external beam radiotherapy treatment using GAMOS/Geant4 Pedro Arce Juan Ignacio

GAMOS radiotherapy optimisation WC2009, Sept. 12th 2009 1

Optimization of an external beam radiotherapy treatment using

GAMOS/Geant4

Pedro ArceJuan Ignacio Lagares

Daniel Pérez-Astudillo (CIEMAT, Madrid)

John ApostolakisGabriele Cosmo

(CERN, Geneva)

World Congress 2009, Munich, 12 September 2009


1. GAMOS radiotherapy framework RT simulation in Geant4 GAMOS RT: geometry GAMOS RT: source GAMOS RT: scoring GAMOS RT: phase space files GAMOS RT: dose in phantoms

Outline

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


GAMOS Radiotherapy framework


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

Radiotherapy simulation with GEANT4


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 0. 4. 0. 20. ($PC_ZMAX-$PC_ZMIN)/2. G4_AIR // continuation line:PLACE "primary collimator" 1 expHall RM0 0. 0. $PC_ZMIN+$PC_ZMAX)/2.:PLACE "primary collimator_hole" 1 "primary collimator" RM0 0. 0. 0. :COLOR "primary collimator" 1. 0. 1. // RGB color

Movements: Any volume can be moved with a user command

GAMOS Radiotherapy frameworkGeometry


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 Radiotherapy frameworkSource (primary generator)

/gamos/generator/directionDist my_source GmGenerPositionDiscGaussian 1*mm/gamos/generator/timeDist my_source GmGenerTimeDecay


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

GAMOS Radiotherapy frameworkScoring


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

GAMOS Radiotherapy frameworkPhase space files

VRML view of VARIAN 6000 accelerator


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!

GAMOS Radiotherapy frameworkDose scoring in phantoms

gMocren visualisation of DICOM geometry and tracks


Dose visualisation

IMRT dose visualisation with CIEMAT’s tool (MIRAS)


Optimisation:

physics cuts


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


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 anAutomatic determination of best production cuts or user limits in

one job


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

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…

Accelerator: Automatic optim. prod cuts

log10(range) of gammas created at target that reach the patient or produce a secondary that reaches the patient

CUT


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…

Dose: Automatic optim. prod cuts

Percent Depth Dose curve for all particles and particles rejected by cut 1mm/e- 10

mm/gamma


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)

RESULTS: Optim. production cuts

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


Optimisation:

Geant4 electromagnetic

physics parameters


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 = 0.05Option2: Rover range = 0.8 Lambda factor = 0.2

Msc step limitation = MinimalDetailed study can be found at http://fismed.ciemat.es/GAMOS/RToptim


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)

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

PDD: new/old


No energy loss fluctuations

Phase space: - Reduces substantially number of low energy (< 0.2 MeV particles)

Dose: - Increases dose by 0.2 - 0.3 % quite uniformly

CPU gain: 13 % default cuts 1 % optimised cuts

CONCLUSIONS: acceptable, but not really needed

PDD: new/old

phase space E: new/old


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


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 cuts20 % optimised cuts

CONCLUSIONS: not acceptable, too big change, not uniform

PDD: new/old

Fewer e-/e+ steps option 2


Optimisation:

Fast dose calculation in

phantom voxels


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

Fast voxel navigation algorithm

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


Optimisation:

CPU time comparison with

BEAMnrc/DOSXYZnrc


VARIAN 2100 gamma accelerator:

106 events on Pentium Dual-Core 3 GHz

BEAMnrc GAMOS/GEANT4 GAMOS/GEANT4(auto. optim cuts)

6 MeV 277 s 359 s 248 s18 MeV* 1020 s 897 s 599 s

CPU time comparison with BEAMnrc

* Same parameters as for 6MeV


DOSXYZnrc GAMOS/GEANT4 GAMOS/GEANT4(auto. optim cuts)

water 234 s 215 s 167 s

patient 300 s 170 s 166 s

Dose in 104 5x5x3 mm water phantom: 106 events on Pentium Dual-Core 3 GHzDose in 4.5 106 patient, 23 materials: 106 events on Pentium Dual-Core 3 GHz

CPU time comparison with DOSZXYnrc

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

Dependency with number of materials

Need to implement density-changing materials in Geant4…


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/0.001-0.01: User=0.01 Real=0 Sys=0gamma/0.01-0.1: User=2.01 Real=2.45 Sys=0.27gamma/0.1-1: User=19.12 Real=22.05 Sys=1.51gamma/1-10: User=4.25 Real=5.4 Sys=0.3e-/0.0001-0.001: User=0.07 Real=0.1 Sys=0e-/0.001-0.01: User=0.54 Real=0.69 Sys=0.06e-/0.01-0.1: User=4.71 Real=5.41 Sys=0.38e-/0.1-1: User=15.59 Real=18.19 Sys=1.79e-/1-10: User=82.83 Real=98.62 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


Optimisation:

Particle splitting


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


Particle splitting: RESULTS

UNIFORM BREMSSTRAHLUNG SPLITTING

Maximum efficiency gain: 2.2

Z-PLANE DIRECTION BREMSSTRAHLUNG SPLITTING

Maximum efficiency gain: 6.5

EQUAL-WEIGHT SPLITTING

Maximum efficiency gain: 45

EGSnrc results with same accelerator: gain 80

Algorithm in GAMOS is not fully implemented yet…


Documentation, installation,

usage


Documentation

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


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)


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


Summary And

Future prospects


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


Future prospects

Improve particle splitting (factor ~ 2 - 3)

Implement history repetition (factor ~ 2 - 5)

Fast Monte Carlo (factor ~ 10 - 100)

Provide standard and fast Monte Carlo in the same

framework Thanks to Geant4 flexibility

Everybody is invited to contribute!


http://fismed.ciemat.es/GAMOS

Documents

GAMOS radiotherapy optimisation WC2009, Sept. 12th 2009 1 Optimization of an external beam radiotherapy treatment using GAMOS/Geant4 Pedro Arce Juan Ignacio