Maria Grazia Pia, INFN Genova Technology transfer from HEP Technology transfer from HEP computing to the medical computing to the medical field field F. Foppiano 3 , S. Guatelli 2 , J. Moscicki 1 , M.G. Pia 2 , M. Piergentili 2 CERN 1 INFN Genova 2 National Institute for Cancer Research, IST Genova 3 Topical Seminar on Innovative Radiation Detectors Siena, 23-26 May 2004 http://www.ge.infn.it/geant4/talks S. Agostinelli, S. Garelli (IST Genova) L. Archambault, L. Beaulieu, J.-F. Carrier, V.-H. Tremblay (Univ. Laval) M.C. Lopes, L. Peralta, P. Rodrigues, A. Trindade (LIP Lisbon) G. Ghiso (S. Paolo Hospital, Savona) Including contributions from:
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Maria Grazia Pia, INFN Genova Technology transfer from HEP computing to the medical field F. Foppiano 3, S. Guatelli 2, J. Moscicki 1, M.G. Pia 2, M. Piergentili.
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Maria Grazia Pia, INFN Genova
Technology transfer from HEP Technology transfer from HEP computing to the medical fieldcomputing to the medical field
F. Foppiano3, S. Guatelli2, J. Moscicki1, M.G. Pia2, M. Piergentili2
CERN1
INFN Genova2
National Institute for Cancer Research, IST Genova3
Topical Seminar on Innovative Radiation Detectors Siena, 23-26 May 2004
http://www.ge.infn.it/geant4/talks
S. Agostinelli, S. Garelli (IST Genova)L. Archambault, L. Beaulieu, J.-F. Carrier, V.-H. Tremblay (Univ. Laval)
M.C. Lopes, L. Peralta, P. Rodrigues, A. Trindade (LIP Lisbon) G. Ghiso (S. Paolo Hospital, Savona)
Including contributions from:
Maria Grazia Pia, INFN Genova
A real life case
A dosimetric system for A dosimetric system for brachytherapy derived from HEP brachytherapy derived from HEP
computingcomputing(but all the developments and applications presented in this talk are general)
Technology transferTechnology transfer
Activity initiated at IST Genova, Natl. Inst. for Cancer Research (F. Foppiano et al.)– hosted at San Martino Hospital in Genova (the largest hospital in Europe)
Collaboration with San Paolo Hospital, Savona (G. Ghiso et al.)– a small hospital in a small town
Maria Grazia Pia, INFN Genova
The goal of radiotherapyThe goal of radiotherapy
Delivering the required therapeutic dose to the tumor area with high precision,
while preserving the surrounding healthy tissue
Dosimetry system precision accurate model of the real configuration (from CT)
speed adequate for clinical use user-friendly interface for hospital usage
Calculate the dose released to the patient by the
radiotherapy system
Accurate dosimetry is at the basis of radiotherapy treatment planning
Maria Grazia Pia, INFN Genova
The realityThe reality
Treatment planning is performed by means of commercial software
The software calculates the dose distribution delivered to the patient in a given source configuration
Open issues
PrecisionPrecision CostCost
Commercial systems are based on approximated analytical methods,approximated analytical methods, because of speed constraints
Approximation in geometry modelinggeometry modeling
Approximation in material modeling material modeling
Each treatment planning software is specific to one techniquespecific to one technique and one type of sourceone type of source
Treatment planning software is expensiveexpensive
Maria Grazia Pia, INFN Genova
Commercial factorsCommercial factorsCommercial treatment planning systems are governed by commercial rules (as any other commercial product...)
i.e., they are produced and marketed by a company only if the investment for development is profitable
No commercial treatment planning systems are available for non-conventional radiotherapy techniques such as hadrontherapyhadrontherapy
or for niche applications such as superficial brachytherapysuperficial brachytherapy
0
20
40
60
80
100
-20 -15 -10 -5 0 5 10 15 20
Distance from the centre (mm)
Sig
nal %
Film X
Microcubes X
Treatment planning systems for hadrontherapy are quite primitive
not commercially convenient so far
Maria Grazia Pia, INFN Genova
Monte Carlo methods in radiotherapyMonte Carlo methods in radiotherapy
Monte Carlo methods have been explored for years as a tool for precise dosimetry, in alternative to analytical methods
de facto,
Monte Carlo simulation is not used in clinical practice
(only side studies)
The limiting factor is the speedspeedOther limitations: reliable? for “software specialists only”, not user-friendly for general practice requires ad hoc modeling
Maria Grazia Pia, INFN Genova
CT-simulation with a Rando phantomExperimental data with TLD LiF dosimeter
CT images used to define the geometry:
a thorax slice from a Rando
anthropomorphic phantom
Comparison with commercial treatment
planning systems
Comparison with commercial treatment
planning systems
M. C. LopesIPOFG-CROC Coimbra Oncological Regional Center
L. Peralta, P. Rodrigues, A. TrindadeLIP - Lisbon
Central-Axis depth dose
Profile curves at 9.8 cm depth
PLATO overestimates the dose at ~ 5% level
Maria Grazia Pia, INFN Genova
M. C. Lopes1, L. Peralta2, P. Rodrigues2, A. Trindade2
Head and neck with two opposed beams for a 5x5 and 10x10 field size
A more complex set-upA more complex set-up
An off-axis depth dose taken at one of the slices near the isocenter
PLATO fails on the air cavities and bone structures and cannot predict accurately the dose to tissue that is surrounded by air
Deviations are up to 25-30%
Beam planeSkull bone
Tumor
Air
Bone
In some tumours sites (ex: larynx T2/T3-stage) a 5% underdosage will decrease local tumour
control probability from ~75% to ~50%
Maria Grazia Pia, INFN Genova
The challenge
Maria Grazia Pia, INFN Genova
Develop a Develop a general purposegeneral purpose
precise precise dosimetric system
with the capability of
realistic geometryrealistic geometryand material modelingand material modeling
interface to CT imagesinterface to CT images
with a user-friendly interfaceuser-friendly interface
atat low costlow cost
adequate adequate speedspeed for clinical usage for clinical usageperforming atperforming at
Maria Grazia Pia, INFN Genova
PrecisionPrecision
Accurate model of the real experimental set-upAccurate model of the
real experimental set-up
Easy configuration for hospital usage
Easy configuration for hospital usage
SpeedSpeed
Calculation of 3-D dose distribution3-D dose distribution in tissueDetermination of isodose isodose curves
Based on Monte CarloMonte Carlo methodsAccurate description of physicsphysics interactionsExperimental validationvalidation of physics involved
Realistic description of geometrygeometry and tissuetissuePossibility to interface to CT images
Simple user interface + Graphic visualisation Elaboration of dose distributionsdose distributions and isodosesisodoses
ParallelisationParallelisationAccess to distributed computing resourcesdistributed computing resources
Other requirementsOther requirementsTransparentTransparentOpen to extension extension and new functionalityPublicly accessiblePublicly accessible
RequirementsRequirementsRequirementsRequirements
Maria Grazia Pia, INFN Genova
PrecisionPrecisionPrecisionPrecision
Based on Monte Carlo methodsMonte Carlo methods
Extension of electromagnetic interactions down to low energies (< 1 keV)
Microscopic validation of the physics modelsMicroscopic validation of the physics modelsComparison Comparison with experimental data experimental data
specific to the brachytherapic practice
Accurate description of physicsphysics interactions
Experimental validationvalidation of physics involved
Maria Grazia Pia, INFN Genova
Code and documentation publicly distributed from web
1st production release: end 1998– 2 new releases/year since then
Developed and maintained by an international collaboration of physicists and computer scientists
Run, Event and Track management PDG-compliant Particle management Geometry and Materials Tracking Detector response User Interface Visualisation Persistency Physics Processes
Maria Grazia Pia, INFN Genova
Barkas effect (charge dependence)models for negative hadrons
e,down to 250 eV
EGS4, ITS to 1 keVGeant3 to 10 keV
Hadron and ion models based on Ziegler and ICRU data and parameterisations
Based on EPDL97, EEDL and EADL evaluated data libraries
Bragg peak
shell effects
antiprotons
protonsions
Fe lines
GaAs lines
Atomic relaxation Fluorescence
Auger effect
Based on Penelope analytical models
Maria Grazia Pia, INFN Genova
Validation
Microscopic validation:
verification of Geant4 physicsverification of Geant4 physics
Dosimetric validation: in the experimental contextin the experimental context
Maria Grazia Pia, INFN Genova
proton straggling
ions
e-, Sandia database
Al
NISTGeant4-LowEGeant4-Standard
Stopping power
Microscopic validationMicroscopic validation
many more
validation results
available!
2N-L=13.1 – =20 - p=0.87
NISTGeant4-LowEGeant4-Standard
Photon attenuation coefficient
Al
2N-S=23.2 – =15 - p=0.08
Maria Grazia Pia, INFN Genova
Dosimetric validationDosimetric validation
0 10 20 30 40 500,0
0,2
0,4
0,6
0,8
1,0
1,2 Simulazione Nucletron Misure
Dose %
Distanza lungo Z (mm)Distance along Z (mm)
SimulationNucletronData
F. Foppiano et al., IST Genova
Comparison to
manufacturer data, protocol data,
original experimental data
experimental mesurements
G. Ghiso, S. Guatelli S. Paolo Hospital Savona
Ir-192 I-125
Maria Grazia Pia, INFN Genova
General purpose systemGeneral purpose systemGeneral purpose systemGeneral purpose system
Object Oriented technologySoftware system designed in terms of Abstract Interfaces
Abstract Factory design patternSource spectrum and geometry transparently interchangeableSource spectrum and geometry transparently interchangeable
For any brachytherapy technique
For any source type
Maria Grazia Pia, INFN Genova
Flexibility of modelingFlexibility of modeling
CT DICOM interface
through Geant4 parameterised volumesGeant4 parameterised volumes parameterisation function: materialparameterisation function: material
Abstract Factory
Configuration of
any brachytherapy technique any brachytherapy technique
any source type any source type
through an Abstract FactoryAbstract Factory to define geometry, primary geometry, primary spectrumspectrum
Phantom
various materialsvarious materials water, soft tissue, bone, muscle etc.
General purpose software system for brachytherapy
No commercial general software exists!
Maria Grazia Pia, INFN Genova
Realistic model Realistic model of the experimental set-upof the experimental set-up
Realistic model Realistic model of the experimental set-upof the experimental set-up
Spectrum (192IrIr, 125II)Geometry
Phantom with realistic material modelPhantom with realistic material modelPossibility to interface the system to CT imagesPossibility to interface the system to CT images
Radioactive source
Patient
Maria Grazia Pia, INFN Genova
Modeling the source geometry
Modeling the source geometry
Precise geometry and material model of any type of source
prototype for an intermediate layer between applications and the GRID
Hide complex details of underlying technology
Developed by J. Moscicki, CERN
http://cern.ch/DIANE
R&D in progress forR&D in progress forLarge Scale Large Scale
Master-WorkerMaster-Worker ComputingComputing
DIANEDIANE
Parallelisation Access to the GRID
Transparent access to a distributed computing environment
Maria Grazia Pia, INFN Genova
Performance: parallel mode on a local cluster
Performance: parallel mode on a local cluster
1M events
4 minutes 34’’
5M events
4 minutes 36’’
1M events
4 minutes 25’’
on up to 50 workers, LSF at CERN, PIII machine, 500-1000 MHz
Performance adequate for clinical application, but…
it is not realistic to expect any hospital to own and maintain a PC farm
Endocavitary brachytherapy
Interstitial brachytherapy
Superficial brachytherapy
preliminary: further optimisation in progress
Maria Grazia Pia, INFN Genova
Running on the GRIDRunning on the GRIDVia DIANE
Same application code as running on a sequential machine or on a dedicated cluster
– completely transparent to the user
A hospital is not required to own and maintain extensive computing resources to exploit the scientific advantages of Monte Carlo simulation for radiotherapy
Any hospital
– even small ones, or in less wealthy countries, that cannot even small ones, or in less wealthy countries, that cannot afford expensive commercial software systemsafford expensive commercial software systems –
may have access to advanced software technologies and tools for radiotherapy
General dosimetry system for radiotherapyGeneral dosimetry system for radiotherapy extensible to other techniques
plug-ins for external beamsplug-ins for external beams
((factories for beam, geometry, physics...)
Configuration of
any brachytherapy technique any brachytherapy technique
any source type any source type
Plug-ins in progress
System extensible to any source configuration
without changing the existing code
treatment headtreatment head hadrontherapyhadrontherapy ......
Maria Grazia Pia, INFN Genova
A medical accelerator for IMRTA medical accelerator for IMRT
Build a simulation tool which determines the dose distributions given in a phantom by the head of a linear accelerator used for IMRT.
Many algorithms were developed to estimate dose distributions, but even the most sophisticated ones resort to some approximations. These approximations might affect the outcome of dose calculation, especially in a complex treatment planning as IMRT.
This microscopic control allows IMRT to produce dose distribution patterns that are much closer to the desired patterns
than possible previously
Maria Grazia Pia, INFN Genova
The user can choose the energy and standard deviation of the primary particles energy distribution (Gaussian)
The primary particles (e-) leave from a point source with random direction (0˚< θ < 0.3˚) and a gaussian distribution
The head components modeled include: target, primary and secondary collimators, vacuum window, flattening filter, ion chamber, mirror, vacuum and air
Each pair of jaws can be rotated through an axis that is perpendicular to the beam axis
The actual analysis produces some histograms from which the user can calculate the Percent Depth Dose (PDD) and the flatness at the following depths in the phantom: 15 mm, 50 mm, 100 mm and 200 mm.