An Overview of Monte CarlAN OVERVIEW OF MONTE CARLO TREATMENTo Treatment

5/26/2018 An Overview of Monte CarlAN OVERVIEW OF MONTE CARLO TREATMENTo Treatment

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AN OVERVIEW OF MONTE CARLO TREATMENTPLANNING FOR RADIOTHERAPYEmiliano Spezi* and Geraint Lewis

Department of Medical Physics, Velindre Cancer Centre, Velindre Road, Whitchurch,Cardiff CF14 2TL, Wales, UK

The implementation of Monte Carlo dose calculation algorithms in clinical radiotherapy treatment planning systems has beenanticipated for many years. Despite a continuous increase of interest in Monte Carlo Treatment Planning (MCTP), its intro-duction into clinical practice has been delayed by the extent of calculation time required. The development of newer and fasterMC codes is behind the commercialisation of the first MC-based treatment planning systems. The intended scope of thisarticle is to provide the reader with a compact primer on different approaches to MCTP with particular attention to thelatest developments in the field.

INTRODUCTION

Recent statistics have shown that radiotherapy con-tributes to cure of 40% of cancer patients in theUK(1). However, radiotherapy represents only 5% ofthe total National Health Service expenditure incancer care(2). Radiotherapy can therefore be con-sidered to be a clinically important and cost-effectiveform of cancer treatment. It has been shown thatdose differences up to 7% can be clinically detectableand that deviations from the prescribed dose of 5%or more can compromise tumour response andtissue morbidity(3,4). The introduction into clinicalpractice of more accurate algorithms for patientdose calculation is therefore of paramount import-

ance. Dose calculation algorithms based on theMonte Carlo (MC) method are widely regarded asthe most accurate tool available in radiotherapy(5).A number of general-purpose MC codes are publiclyavailable (e.g. FLUKA(6), MCNP(7), EGSnrc(8),Penelope(9), GEANT(10)) and have been intensivelyused for research and development in medical appli-cations over the past decades. Recently, Rogers(11)

reviewed techniques for electronphoton transportsimulations with special emphasis on theEGS4/EGSnrc code system. Ma and Jiang(12)

reviewed MC modelling techniques of clinical elec-tron beams, whereas Verhaegen and Seuntjens(13)

focused their review on the modelling of externalradiotherapy photon beams. Figure 1 summarisesthe number of publications between 1985 and 2007for general-purpose MC codes of major interest inmedical physics (http://www.isiknowledge.com).Entries are grouped per scientific area. It is evidentthat the MCNP code is used extensively in differentscientific fields. This is because of the flexibility and

usability provided by the combinatorial geometry

package and because of the possibility to transportall particles (i.e. neutrons, protons and heavy ions aswell as photons and electrons). However, in medi-cally related fields the EGS4/EGSnrc code system isvery popular, representing a de facto standardfor photon electron transport in the energy range(110 MeV) of radiotherapy interest.

In particular, the number of publications reportingthe use of MC in radiotherapy treatment planning(MCTP) has increased exponentially in the last25 years. This is clearly shown in Figure 2, wherethe number of MCTP scientific articles between1985 and 2007 is sorted per year of publication.Despite a continuous increase of interest in MCTP,the introduction of MC algorithms in clinical prac-tice has been delayed by the excessive calculationtimes involved. The development of newer and fasterMC codes is behind the first wave of commerciallyavailable MC-based treatment planning systems.Reynaert et al.(14) have recently discussed existingMC dose calculation engines and reported onspecific issues regarding the commissioning of MCTP systems. Similarly Chetty et al.(5) havereviewed major MC codes being used in clinicalapplications and provided useful recommendationsfor the clinical implementation of MCTP.

The intended scope of this article is to provide the

reader with a compact primer on the differentapproaches to MCTP rather than an exhaustivereview of the available MC codes and techniques,which have been already covered elsewhere.

A BRIEF HISTORY OF MCTP

Several MC codes have been developed and used inmedical radiation physics in the past few decades.Figure 3 summarises the major release of popularMC codes used in radiotherapy physics, radiation

*Corresponding author: [email protected], [email protected]

# The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]

Radiation Protection Dosimetry (2008), pp. 17 doi:10.1093/rpd/ncn277

Radiation Protection Dosimetry Advance Access published October 16, 2008
http:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.isiknowledge.comhttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.isiknowledge.comhttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.isiknowledge.comhttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.isiknowledge.comhttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.isiknowledge.com


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Figure 1. Number of publications between 1985 and 2007 for main MC codes of interest in radiation therapy.Publications are sorted by application field (source: ISI Web of knowledge).

Figure 2. Number of publications on MCTP between 1985 and 2007 (source: ISI Web of knowledge). The exponentialgrowth is highlighted by the solid trend line.

E. SPEZI AND G. LEWIS

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protection and treatment planning in the last25 years: related codes are linked together alongfamily lines.

The release of the BEAM code system in 1995(15)

undoubtedly represents a milestone in the use of theMC technique in the simulation of radiotherapylinear accelerators (linacs). BEAM provided the userwith an extensive set of predefined componentmodules that could be used for the simulation ofindividual elements of the linac, without the need to

rewrite the routines for the transport of the particleseach time they were required. BEAM also providedsome innovative features such as the possibility togenerate phase space (phsp) files by saving location,energy, direction and type of particle crossing aspecific plane in space. phsp files can be reused forsubsequent simulation of downstream elements ofthe linac, such as beam modifiers, for the simulationof the irradiation of a patient, or for the characteris-ation of the radiation source and the generation ofvirtual source models. A recent review of the currentstatus of MCTP in Europe(16) has shown that 66%of responding centres use the BEAM code forresearch and development in radiotherapy physics.

The introduction of variance reduction tech-niques leads to increased efficiency of MC simu-lations, and the development of new MC codesoptimisedfor radiotherapy dose calculation, such asXVMC(17), VMC(18) and DPM(19), has reducedpatient calculation time significantly. This has beendemonstrated first for electrons and then for photons(in some cases, with speed increasing by a factor of50 or more). However, current radiotherapy treat-ments involving heavy beam modulation, such asintensity modulated radiation therapy (IMRT),

demands the continuous generation of patient-specific fluence maps leaving scope for distributingMC simulations on multiple processors.

USE OF MC IN TREATMENT PLANNING

Accurate clinical dose calculation can be computed,provided that the radiation source and the patientanatomy are modelled correctly. Although it is fore-seeable that newer MC codes will allow one to simu-

late both radiation transport through the linac anddose delivered to the patient in one fast simulation,until now a common approach has been to split cal-culations into two parts: source and patientsimulation.

Source simulation

The simulation of the radiation source can beaccomplished in several ways. One option is tomodel particle interaction in the treatment head andto save a phsp file above the beam collimators. Inthis case, the upper part of the linac (containingfixed components) is simulated only once (Figure4).Treatment-specific beam collimation (involvingmoving components) can then be simulated bysaving a second phsp file which will be used to feedpatient dose calculation. Alternatively, the patientvolume can be integrated into the transport geo-metry, and both beam collimation and patient simu-lation can be carried out at the same time.

A second option is to use a virtual sourcemodel(5) to feed the treatment-specific beam colli-mation. Virtual source models are parameterisationsof the linac head and can be built from phsp files or

Figure 3. Timeline of the release of major versions of MC codes used in medical radiation physics from the 1980s to the

present.

OVERVIEW OF MCTP FOR RADIOTHERAPY

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from measurements. Source models have threeadvantages over phsp files: (1) they do not requirelarge disk space for storage, (2) they can be opti-mised automatically for different linacs and (3) theyare not susceptible to noise which can affect phspfiles when a relatively low number of particles arerecorded.

Patient simulation

Patient data can be processed in two ways. Acommon approach is to convert CT numbers inmaterials and densities using a predefined CT con-version function. This is shown in Figure 5 wherethe original diagnostic scan in DICOM (DigitalImaging and Communication in Medicine Standard:http://www.nema.org/stds) format (Figure 5a) isconverted (for demonstration purposes) to a text file(Figure 5b) where material number and density arestored for each voxel of the calculation grid usingthe conversion function in Figure 5c. During MCsimulation, the appropriate cross-section data foreach material are sourced from the database whenthe particles enter a specific voxel. A secondapproach, which has been implemented in theXVMC/VMC codes, is based on the consider-ation that the variation of mass and radiation stop-ping powers with density can be expressed by asimple relationship for biological tissues at energiesof therapeutic interest(17). In this method, CTnumbers are converted to mass densities which arethen used to sample interaction probabilities usingstopping power ratio to water. This technique has

the advantage of providing a continuous conversionmechanism between CT numbers and interactionprobabilities. Recently, Vanderstraeten et al.(20)

developed a CT conversion method though a stoi-chiometric CT calibration carried in a Europeanmulticentre study. This study has shown the feasi-bility of using dosimetrically equivalent tissuesubsets for clinical photon beams and highlighted

Figure 5. Patient modelling: conversion of Hounsfieldnumbers (a) into materials and densities (b) using a

predefined CT conversion function (c).

Figure 4. Invariant and patient-specific parts of aradiotherapy linac. Patient data and electronic portalimaging devices used for imaging and in vivo dosimetry

purposes are also shown.


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the need for multiple bone bins when MC calcu-lations of patient dose distributions are computedand compared to dose from conventional treatmentplanning systems.

RECENT APPROACHES TO MCTP

A number of MC-based treatment planning systems(TPS) are commercially available today. Althoughphoton MCTP is reportedly due to be commercia-lised later this year by several vendors, electrons arethe prevalent treatment modality in MCTP. Asrecently pointed out(16), new releases of MC-basedTPS will continue to use MC technology only forthe final dose calculation and not in the plan optim-isation phase. This is because full inverse MC treat-ment planning is not yet a practical option due tocalculation time and to the number of times thesimulations need to be repeated within an iterativeoptimisation loop. However, it is common to refer to

Monte Carlo as an embarrassingly parallel problem,that is, a problem which can easily be parallelised ordistributed in supercomputer clusters. Several papershave been published about MC-based radiotherapyplanning environments using supercomputer facilitiesto support intensive calculation requirements. Thissection will review some recent approaches.

MCRTV

An integrated system for MC routine radiotherapyplan verification (MCRTV)(21) has been developed atthe Osaka University Graduate School of Medicine.

The system is built on the EGS4 MC code anddivides the patient simulation into three steps:(1) simulation of the invariant part of the linacshead, (2) simulation of the patient-specific beammodulation and (3) simulation of the patientirradiation. During routine radiotherapy dose verifi-cation, only patient-specific beam modulation andpatient dose calculation are performed: the invariantpart of the linacs head is not simulated but rep-resented by a phsp file. MCRTV has been integratedwith the Eclipse TPS (Varian Medical Systems, PaloAlto, CA). Linac and patient information aresourced from DICOM-RT files, whereas the multileaf collimator (MLC) fields are read from Variansproprietary file format. The three MC codes used inthe above steps have been parallelised using MessagePassing Interface (MPI; http://www-unix.mcs.anl.gov/mpi) and MC simulations are sent to a dedi-cated computer cluster. MCRTV is not yet publiclyavailable.

MMCTP

The McGill radiotherapy research environment forMC treatment planning (MMCTP), developed at

McGill University(22), is a multi-platform (Windows,Linux, Macintosh) software system that implementsthe BEAMnrc and XVMC codes for patient-specifictreatment planning. The two MC codes are used insequence to calculate the final dose to the patient.BEAMnrc is used for treatment head simulation,whereas XVMC is used for patient dose calculation.The system runs on a single workstation, supportsinput in DICOM-RT, RTOG (Radiation TherapyOncology Group format: http://itc.wustl.edu/exchange_files/tapeexch400.htm) and Varian CARTformat, although the internal data format is anenhancement of the RTOG format called McGillRT. Simulations are run on a remote cluster,through the MMCTP graphical user interface(GUI), using standard protocols such as secure shell(SSH). A set of daemons running on the back-ground checks the status of the simulations, addingphsp files or downloading dose distribution fileswhen calculations are complete. Patient data are

anonymised. MMCTP also provides contouringsoftware and data analysis tools such as DVHs. Thesystem is not yet publicly available for download.

SMCP

The Swiss Monte Carlo Plan (SMCP) developed atthe University Hospitalof Berne has been interfacedwith the Eclipse TPS(23). The authors of the codehave developed a flexible environment in which MCsimulations can be initiated from within the EclipseTPS through a dedicated GUI where the user canadjust several simulation parameters. Simulations

are sent to a remote cluster and monitored. Whensimulations are completed, the results are collectedand transferred back for analysis using all the fea-tures of the TPS. This software implementsEGSnrc(8), VMC(18), analytical anisotropicalgorithm (AAA)(24) and PIN, an in-house MC codefor the simulation of photon interactions. Since par-ticle transport and geometry are decoupled, the usercan mix and match the codes used for the simulationof a radiotherapy treatment. The source above beammodifiers, such as dynamic wedges or MLCs, can besimulated using phsp files, source models, the AAAbeam model or full head simulation usingVMC. Patient-specific beam modifiers can besimulated using EGSnrc, VMC or PIN. Patientdose calculation can be carried out using EGSnrc orVMC. No public release of the code has beenmentioned.

RTGRID

The RTGrid distributed simulation environmentfor conformal radiotherapy(25) was developed atCardiff University and Velindre Cancer Centre.In the RTGrid environment, MC simulations of


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radiotherapy treatment plans can be submitted to acomputational resource via a web portal (http://www.wesc.ac.uk/projectsite/rtgrid). The user canupload the MC input and data files in the portaland start the experiment. A DICOM server is alsoavailable to receive computation requests from theTPS and automatically start the experiment, as analternative submission method. Remote simulationsare monitored and results are available for downloadfrom the RTGrid web portal when the calculation isfinished, or available in DICOM format in theRTGrid server. Currently configured resources are(1) the Cardiff University condor pool, (2) VelindreCancer Centre condor pool and (3) the UKNational Grid Service. The software implements theBEAMnrc code system that is used for the concur-rent transport of particles through both linac andthe patient. The RTgrid database contains XMLexperiment profiles which are parametrised with anextension mechanism to allow the easy configuration

for a particular simulation case. User authenticationis established through a username/password combi-nation or through the use of a grid certificate.RTGrid is one of the first examples of grid-enabledcomputing in radiotherapy physics.

CURRENT STATUS AND FUTUREPERSPECTIVE

At the time this article is written, only three com-mercial MC systems are available in the market andonly one supports photons. However, the number ofsystems for MC-based retrospective verification of

radiotherapy plans and prospective planning is incontinuous growth. In addition to this, the numberof commercial MC-based TPS is expected to doublein 2008 with more than 30 centres, in Europe alone,currently evaluating commercial MCTP technology.It is foreseen that only one vendor out of six willprovide a full MC-based IMRT optimisation solu-tion(16). Therefore, it is reasonable to think thatMCTP will remain a forward planning tool for thetime being. Nonetheless, since MC is a very generaland flexible method, it becomes advantageous as thecomplexity of the problem increases(27). In fact, inanalogy with the simulation of dynamic wedges ordynamic MLCs, the MC method is very well suitedfor the simulation of radiotherapy treatmentsaccounting for respiratory motion(2629). This isbecause the voxel dataset representing the patientcan be moved mathematically during the MC dosecomputation to simulate patient motion, with noadditional computational overhead.

Another field of application of MCTP is nuclearmedicine(30). With the advance of imaging modalitiessuch as positron emitted tomography in identifyingand monitoring patients response to cancer treat-ment and molecular targeted radiotherapy, it is very

likely that MC will play a very important role in theassessment of the dose delivered to the patient byinternal emitters(31). Recent investigations haveshown the effectiveness of using MC modelling ofthe dose delivered to skeletal metastases by bone-seeking radiopharmaceuticals(32).

Another challenging area of investigation and pro-ductive application for MCTP is represented by thesimulation of exit dosimetry. Using MCTP techno-logy, it is indeed possible to predict accurately theexit dose recorded in electronic portal imagers (cf.Figure4), overcoming the limitations of convention-al calculation methods in modelling all differentscattering conditions(33). MCTP can then be used tovalidate radiotherapy treatments by comparing themwith in vivo dosimetry. Furthermore, patient dosecan be reconstructed using MC-based scatter-corrected exit dose maps and back-projection algor-ithms for daily patient-specific dosimetry andverification of patient movement(34).

FUNDING

The financial support of Cancer Research Wales andYr Ysgol Uwchradd Tregaron is greatly appreciated.

REFERENCES

1. National Radiotherapy Advisory Group. Radiotherapy:delivering a world class service for England(Department of Health) (2007).

2. Department of Health. Cancer reform strategy(Department of Health, London) (2007). See alsohttp://www.dh.gov.uk/publications.

3. Dische, S., Saunders, M. I., Williams, C., Hopkins, A.and Aird, E. Precision in reporting the dose given in acourse of radiotherapy. Radiother. Oncol. 29(3),287293 (1993).

4. Papanikolau, N., Battista, J. J., Boyer, A. L., Kappas,C., Klein, E., Mackie, T. R., Sharpe, M. and Van Dyk,J. Tissue inhomogeneity corrections for megavoltage

photon beams.AAPM Report No. 85 (Medical PhysicsPublishing, Madison, WI) (2004).

5. Chetty, I. J.et al. Report of the AAPM Task Group No.105: issues associated with clinical implementation ofMonte Carlo-based photon and electron external beamtreatment planning. Med. Phys. 34, 48184853 (2007).

6. Fasso, A., Ferrari, A., Ranft, J. and Sala, P. R.

FLUKA: a multi-particle transport code. CERN-2005-10, INFN/TC_05/11, SLAC-R-773 (2005).7. Brown, F. B. MCNPa general Monte Carlo-particle

transport code, version 5. Report LA-UR-03 1987 (LosAlamos National Laboratory, Los Alamos, NM)(2003).

8. Kawrakow, I. and Rogers, D. W. O. The EGSnrc codesystem: Monte Carlo simulation of electron and photontransport. Technical Report PIRS-701 (NationalResearch Council of Canada, Ottawa, Ont.) (2000).

9. Baro, J., Sempau, J., Fernandez-Varea, J. M. andSalvat, F. PENELOPEan algorithm for Monte-Carlosimulation of the penetration and energy-loss of


Page 6 of 7
http://%28http//www.wesc.ac.uk/projectsite/rtgrid).http://%28http//www.wesc.ac.uk/projectsite/rtgrid).http:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.dh.gov.uk/publicationshttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.dh.gov.uk/publicationshttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.dh.gov.uk/publicationshttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.dh.gov.uk/publicationshttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.dh.gov.uk/publicationshttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.dh.gov.uk/publicationshttp:///reader/full//%3C?A3B2%20h%20-.1w?%3E/www.dh.gov.uk/publicationshttp://%28http//www.wesc.ac.uk/projectsite/rtgrid).http://%28http//www.wesc.ac.uk/projectsite/rtgrid).


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electrons and positrons in matter. Nucl. Instrum. Meth.Phys. Res. A 100, 3146 (1995).

10. Agostinelli, S. et al. GEANT4a simulation toolkit.Nucl. Instrum. Meth. Phys. Res. A 506, 250303 (2003).

11. Rogers, D. W. O. Fifty years of Monte Carlo simu-lations for medical physics. Phys. Med. Biol. 51,R287R301 (2006).

12. Ma, C. M. and Jiang, S. B.Monte Carlo modelling ofelectron beams from medical accelerators. Phys. Med.Biol. 44, R157R189 (1999).

13. Verhaegen, F. and Seuntjens, J. Monte Carlo modellingof external radiotherapy photon beams. Phys. Med.Biol. 48, R107R164 (2003).

14. Reynaert, N., van der Marck, S. C., Schaart, D. R.,Vander Zee, W., Van Vliet-Vroegindeweij, C., Tomsej, M.,Jansen, J., Heijmen, B., Coghe, M. and De Wagter, C.Monte Carlo treatment planning for photon and electronbeams.Radiat. Phys. Chem. 76, 643686 (2007).

15. Rogers, D. W., Faddegon, B. A., Ding, G. X., Ma, C. M.,We, J. and Mackie, T. R. BEAM: a Monte Carlo codeto simulate radiotherapy treatment. Med. Phys. 22,503524 (1995).

16. Spezi, E. Status of MCTP in Europe. Radiother.Oncol.84, S6S7 (2007).

17. Fippel, M. Fast Monte Carlo dose calculation forphoton beams based on the VMC electron algorithm.Med. Phys. 26, 14661475 (1999).

18. Kawrakow, I. VMC, electron and photon MonteCarlo calculations optimized for radiation treatment plan-ning: advanced Monte Carlo for radiation physics, particletransport simulation and applications. In: Proceedings ofthe Monte Carlo 2000 Meeting, Lisbon, Kling, A. et al., Eds. (Springer, Berlin) pp. 229236 (2001).

19. Sempau, J., Wilderman, S. J. and Bielajew, A. F. DPM,a fast, accurate Monte Carlo code optimized for photonand electron radiotherapy treatment planning dose calcu-lations. Phys. Med. Biol. 45, 22632291 (2000).

20. Vanderstraeten, B.et al. Conversion of CT numbers intotissue parameters for Monte Carlo dose calculations: amulti-centre study. Phys. Med. Biol. 52, 539562(2007).

21. Yamamoto, T., Mizowaki, T., Miyabe, Y., Takegawa,H., Narita, Y., Yano, S., Nagata, Y., Teshima, T. andHiraoka, M. An integrated Monte Carlo dosimetric ver-ification system for radiotherapy treatment planning.Phys. Med. Biol. 52, 19912008 (2007).

22. Alexander, A., Deblois, F., Stroian, G., Al-Yahya, K.,Heath, E. and Seuntjens, J. MMCTP: a radiotherapyresearch environment for Monte Carlo and patient-specific treatment planning. Phys. Med. Biol. 52,N297 N308 (2007).

23. Fix, M. K., Manser, P., Frei, D., Volken, W., Mini, R.

and Born, E. J. An efficient framework for photon

Monte Carlo treatment planning. Phys. Med. Biol. 52,N425 N437 (2007).

24. Ulmer, W., Pyyry, J. and Kaissl, W. A 3D photonsuperposition/convolution algorithm and its foundationon results of Monte Carlo calculations. Phys. Med. Biol.50, 17671790 (2005).

25. Yaikhom, G., Giddy, J. P., Walker, D. W., Downes,

P. E., Spezi, E. and Lewis, D. G. A distributed simu-lation framework for conformal radiotherapy. In:Proceedings of the 22nd IEEE International Paralleland Distributed Processing Symposium (IPDPS)(IEEE Computer Society) 29933000 (2008).

26. Keall, P. J., Joshi, S., Vedam, S. S., Siebers, J. V., Kini,V. R. and Mohan, R. Monte Carlo as a four-dimensional radiotherapy treatment-planning tool toaccount for respiratory motion. Phys. Med. Biol. 49,3639 3648 (2004).

27. Verhaegen, F. Monte Carlo techniques in radiotherapytreatment planning towards a virtual radiotherapydepartment. SCOPE 11, 20 24 (2002).

28. Paganetti, H., Jiang, H., Adams, J. A., Chen, G. T.and Rietzel, E. Monte Carlo simulations with time-

dependent geometries to investigate effects of organmotion with high temporal resolution. Int. J. Radiat.Oncol. Biol. Phys. 60, 942950 (2004).

29. Seco, J., Sharp, G. C., Wu, Z., Gierga, D., Buettner, F.and Paganetti, H. Dosimetric impact of motion in free-breathing and gated lung radiotherapy: a 4D MonteCarlo study of intrafraction and interfraction effects.Med. Phys. 35, 356366 (2008).

30. Zaidi, H. and Andreo, P. Monte Carlo techniques innuclear medicine dosimetry. In: TherapeuticApplications of Monte Carlo Calculations in NuclearMedicine. Zaidi, H. and Sgouros, G. Eds. (Institute ofPhysics Publishing, Bristol, UK) pp. 2854 (2002).

31. Lehmann, J. et al. Monte Carlo treatment planning formolecular targeted radiotherapy within the MINERVA

system.Phys. Med. Biol. 50, 947958 (2005).32. Strigari, L., Sciuto, R., DAndrea, M., Pasqualoni, R.,

Benassi, M. and Maini, C. L. Radiopharmaceuticaltherapy of bone metastases with 89SrCl2,

186Re-HEDPand 153Sm-EDTMP: a dosimetric study using MonteCarlo simulation. Eur. J. Nucl. Med. Mol. Imaging 34,1031 1038 (2007).

33. Chin, P. W., Spezi, E. and Lewis, D. G. Monte Carlosimulation of portal dosimetry on rectilinear voxel geo-metry: a variable gantry angle solution. Phys. Med.Biol.48, N231N238 (2003).

34. Jarry, G. and Verhaegen, F. Patient-specific dosimetryof conventional and intensity modulated radiationtherapy using a novel full Monte Carlo phase spacereconstruction method from electronic portal images.

Phys. Med. Biol. 52, 22772299 (2007).


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