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The use of Monte Carlo particle transport simulations in radiotherapy was pioneered in the early nineteen-seventies, but it was not until the eighties that they gained recognition as an essential research tool for radiation dosimetry, health physics and later on for radiation therapy treatment planning. Since the mid-nineties, there has been a boom in the number of workers using MC techniques in radiotherapy, and the quantity of papers published on the subject. Research and applications of MC techniques in radiotherapy span a very wide range from fundamental studies of cross sections and development of particle transport algorithms, to clinical evaluation of treatment plans for a variety of radiotherapy modalities.

The International Workshop on Current Topics in Monte Carlo Treatment Planning took place at Montreal General Hospital, which is part of McGill University, halfway up Mount Royal on Montreal Island. It was held from 3-5 May, 2004, right after the freezing winter has lost its grip on Canada. About 120 workers attended the Workshop, representing 18 countries. Most of the pioneers in the field were present but also a large group of young scientists. In a very full programme, 41 long papers were presented (of which 12 were invited) and 20 posters were on display during the whole meeting. The topics covered included the latest developments in MC algorithms, statistical issues, source modelling and MC treatment planning for photon, electron and proton treatments. The final day was entirely devoted to clinical implementation issues. Monte Carlo radiotherapy treatment planning has only now made a slow entrée in the clinical environment, taking considerably longer than envisaged ten years ago. Of the twenty-five papers in this dedicated special issue, about a quarter deal with this topic, with probably many more studies to follow in the near future. If anything, we hope the Workshop served as an accelerator for more clinical evaluation of MC applications. The remainder of the papers in this issue demonstrate that there is still plenty of work to be undertaken on other topics such as source modelling, calculation speed, data analysis, and development of user-friendly applications.

We acknowledge the financial support of the National Cancer Institute of Canada, the Institute of Cancer Research of the Canadian Institutes of Health Research, the Research Grants Office and the Post Graduate Student Society of McGill University, and the Institute of Physics Publishing (IOPP). A final word of thanks goes out to all of those who contributed to the successful Workshop: our local medical physics students and staff, the many colleagues who acted as guest associate editors for the reviewing process, the IOPP staff, and the authors who generated new and exciting work.

We are also grateful for the endorsement of the Workshop by the International Atomic Energy Agency, the Canadian Organization of Medical Physicists and the American Association of Physicists in Medicine. [This line was added to the editorial on 23 March 2005.]

Accurate simulation of large electron fields may lead to improved accuracy in Monte Carlo treatment planning while simplifying the commissioning procedure. We have used measurements made with wide-open jaws and no electron applicator to adjust simulation parameters. Central axis depth dose curves and profiles of 6–21 MeV electron beams measured in this geometry were used to estimate source and geometry parameters, including those that affect beam symmetry: incident beam direction and offset of the secondary scattering foil and monitor chamber from the beam axis. Parameter estimation relied on a comprehensive analysis of the sensitivity of the measured quantities, in the large field, to source and geometry parameters. Results demonstrate that the EGS4 Monte Carlo system is capable of matching dose distributions in the largest electron field to the least restrictive of 1 cGy or 1 mm, with D_max of 100 cGy, over the full energy range. This match results in an underestimation of the bremsstrahlung dose of 10–20% at 15–21 MeV, exceeding the combined experimental and calculational uncertainty in this quantity of 3%. The simulation of electron scattering at energies of 15–21 MeV in EGS4 may be in error. The recently released EGSnrc/BEAMnrc system may provide a better match to measurement.

The laser wakefield acceleration (LWFA) mechanism can accelerate electrons to energies within the 6–20 MeV range desired for therapy application. However, the energy spectrum of LWFA-generated electrons is broad, on the order of tens of MeV. Using existing laser technology, the therapeutic beam might require a significant energy spread to achieve clinically acceptable dose rates. The purpose of this work was to test the assumption that a scattering foil system designed for a mono-energetic beam would be suitable for a poly-energetic beam with a significant energy spread. Dual scattering foil systems were designed for mono-energetic beams using an existing analytical formalism based on Gaussian multiple-Coulomb scattering theory. The design criterion was to create a flat beam that would be suitable for fields up to 25 × 25 cm² at 100 cm from the primary scattering foil. Radial planar fluence profiles for poly-energetic beams with energy spreads ranging from 0.5 MeV to 6.5 MeV were calculated using two methods: (a) analytically by summing beam profiles for a range of mono-energetic beams through the scattering foil system, and (b) by Monte Carlo using the EGS/BEAM code. The analytic calculations facilitated fine adjustments to the foil design, and the Monte Carlo calculations enabled us to verify the results of the analytic calculation and to determine the phase-space characteristics of the broadened beam. Results showed that the flatness of the scattered beam is fairly insensitive to the width of the input energy spectrum. Also, results showed that dose calculated by the analytical and Monte Carlo methods agreed very well in the central portion of the beam. Outside the useable field area, the differences between the analytical and Monte Carlo results were small but significant, possibly due to the small angle approximation. However, these did not affect the conclusion that a scattering foil system designed for a mono-energetic beam will be suitable for a poly-energetic beam with the same central energy. Further studies of the dosimetric properties of LWFA-generated electron beams will be done using Monte Carlo methods.

Adjustments made to Monte Carlo models during the commissioning of the simulation should be physically realistic and correspond to actual machine characteristics. Large electron fields, with the jaws fully open and the applicator removed, are sensitive to important source and geometry parameters and may provide the most accurate beam models, including those collimated by an applicator. We report on the results of a comprehensive Monte Carlo sensitivity study documenting the response of these large fields to changes in the configuration of a Siemens Primus linear accelerator. The study was performed for 6, 9 12, 15, 18 and 21 MeV configurations, and included variations of thickness, position and lateral alignment of all treatment head components. Variations of electron beam characteristics were also included in the study. Results were classified by their impact on central-axis depth dose distributions, including the bremsstrahlung tail, and on beam profiles near D_max and in the bremsstrahlung region. Low-energy results show an increased sensitivity to electron beam properties. High-energy bremsstrahlung profiles are shown to be useful in determining misalignments between the beam axis and mechanical isocentre. For all energies, the alignment of the secondary scattering foil and monitor chamber are shown to be critical for correctly modelling beam asymmetries. The results suggest a methodology for commissioning of electron beams using Monte Carlo treatment head simulation.

Extensive work has been performed to validate Monte Carlo models for both photon and electron beams under standard conditions. However, for large field electron beam therapy, Monte Carlo simulations have not been able to provide good agreement when compared to the measured dose distributions. Since the accuracy of the calculation relies heavily on the geometry parameters of the linear accelerator and the characteristics of the incident electron beam, it is crucial to have a complete comprehension of these independent factors. In this work, the electron focal spot size for a CL21EX linac with various energies (6, 9, 12 and 16 MeV) was measured with a slit camera composed of alternating lead and paper sheets. For all the energies investigated, the electron focal spot is found to be elliptical and has a full width at half maximum (FWHM) ranging from 1.69 mm to 2.24 mm. A shift with respect to the crosshair was associated with each measured focal spot. In addition, we present an improved result for the large field in-air profile by utilizing a proposed divergent beam model in conjunction with the experimental focal spot dimension. This model can potentially provide a solution to the Monte Carlo validation of large field electron beam therapy.

Stereotactic radiosurgery with several static conformal beams shaped by a micro multileaf collimator (μMLC) is used to treat small irregularly shaped brain lesions. Our goal is to perform Monte Carlo calculations of dose distributions for certain treatment plans as a verification tool. A dedicated μMLC component module for the BEAMnrc code was developed as part of this project and was incorporated in a model of the Varian CL2300 linear accelerator 6 MV photon beam. As an initial validation of the code, the leaf geometry was visualized by tracing particles through the component module and recording their position each time a leaf boundary was crossed. The leaf dimensions were measured and the leaf material density and interleaf air gap were chosen to match the simulated leaf leakage profiles with film measurements in a solid water phantom. A comparison between Monte Carlo calculations and measurements (diode, radiographic film) was performed for square and irregularly shaped fields incident on flat and homogeneous water phantoms. Results show that Monte Carlo calculations agree with measured dose distributions to within 2% and/or 1 mm except for field size smaller than 1.2 cm diameter where agreement is within 5% due to uncertainties in measured output factors.

In this study, we show that beam model differences play an important role in the comparison of dose calculated with various algorithms for lung cancer treatment planning. These differences may impact the accurate correlation of dose with clinical outcome. To accomplish this, we modified the beam model penumbral parameters in an equivalent path length (EPL) algorithm and subsequently compared the EPL doses with those generated with Monte Carlo (MC). A single AP beam was used for beam fitting. Two different beam models were generated for EPL calculations: (1) initial beam model (init_fit) and (2) optimized beam model (best_fit), with parameters optimized to produce the best agreement with MC calculated profiles at several depths in a water phantom. For the 6 MV, AP beam, EPL(init_fit) calculations were on average within 2%/2 mm (1.4 mm max.) agreement with MC; the agreement for EPL(best_fit) was 2%/0.5 mm (1.0 mm max.). For the 15 MV, AP beam, average agreements with MC were 5%/2 mm (7.4%/2.6 mm max.) for EPL(init_fit) and 2%/1.0 mm (1.3 mm max.) for EPL(best_fit). Treatment planning was performed using a realistic lung phantom using 6 and 15 MV photons. In all homogeneous phantom plans, EPL(best_fit) calculations were in better agreement with MC. In the heterogeneous 6 MV plan, differences between EPL(best_fit and init_fit) and MC were significant for the tumour. The EPL(init_fit), unlike the EPL(best_fit) calculation, showed large differences in the lung relative to MC. For the 15 MV heterogeneous plan, clinically important differences were found between EPL(best_fit or init_fit) and MC for tumour and lung, suggesting that the algorithmic difference in inhomogeneous tissues was most influential in this case. Finally, an example is presented for a 6 MV conformal clinical treatment plan. In both homogeneous and heterogeneous cases, differences between EPL(best_fit) and MC for lung tissues were smaller compared to those between EPL(init_fit) and MC. Although the extent to which beam model differences impact the dose comparisons will be dependent upon beam parameters (orientation, field size and energy), and the size and location of the tumour, this study shows that failing to correctly account for beam model differences will lead to biased comparisons between dose algorithms. This may ultimately hinder our ability to accurately correlate dose with clinical outcome.

IMRT is frequently used in the head-and-neck region, which contains materials of widely differing densities (soft tissue, bone, air-cavities). Conventional methods of dose computation for these complex, inhomogeneous IMRT cases involve significant approximations. In the present work, a methodology for the development, commissioning and implementation of a Monte Carlo (MC) dose calculation engine for intensity modulated radiotherapy (MC-IMRT) is proposed which can be used by radiotherapy centres interested in developing MC-IMRT capabilities for research or clinical evaluations. The method proposes three levels for developing, commissioning and maintaining a MC-IMRT dose calculation engine: (a) development of a MC model of the linear accelerator, (b) validation of MC model for IMRT and (c) periodic quality assurance (QA) of the MC-IMRT system. The first step, level (a), in developing an MC-IMRT system is to build a model of the linac that correctly predicts standard open field measurements for percentage depth–dose and off-axis ratios. Validation of MC-IMRT, level (b), can be performed in a rando phantom and in a homogeneous water equivalent phantom. Ultimately, periodic quality assurance of the MC-IMRT system is needed to verify the MC-IMRT dose calculation system, level (c). Once the MC-IMRT dose calculation system is commissioned it can be applied to more complex clinical IMRT treatments. The MC-IMRT system implemented at the Royal Marsden Hospital was used for IMRT calculations for a patient undergoing treatment for primary disease with nodal involvement in the head-and-neck region (primary treated to 65 Gy and nodes to 54 Gy), while sparing the spinal cord, brain stem and parotid glands. Preliminary MC results predict a decrease of approximately 1–2 Gy in the median dose of both the primary tumour and nodal volumes (compared with both pencil beam and collapsed cone). This is possibly due to the large air-cavity (the larynx of the patient) situated in the centre of the primary PTV and the approximations present in the dose calculation.

Two Monte Carlo dose engines for radiotherapy treatment planning, namely a beta release of Peregrine and MCDE (Monte Carlo dose engine), were compared with Helax-TMS (collapsed cone superposition convolution) for a head and neck patient for the Elekta SLi plus linear accelerator. Deviations between the beta release of Peregrine and MCDE up to 10% were obtained in the dose volume histogram of the optical chiasm. It was illustrated that the differences are not caused by the particle transport in the patient, but by the modelling of the Elekta SLi plus accelerator head and more specifically the multileaf collimator (MLC). In MCDE two MLC modules (MLCQ and MLCE) were introduced to study the influence of the tongue-and-groove geometry, leaf bank tilt and leakage on the actual dose volume histograms. Differences in integral dose in the optical chiasm up to 3% between the two modules have been obtained. For single small offset beams though the FWHM of lateral profiles obtained with MLCE can differ by more than 1.5 mm from profiles obtained with MLCQ. Therefore, and because the recent version of MLCE is as fast as MLCQ, we advise to use MLCE for modelling the Elekta MLC. Nevertheless there still remains a large difference (up to 10%) between Peregrine and MCDE. By studying small offset beams we have shown that the profiles obtained with Peregrine are shifted, too wide and too flat compared with MCDE and phantom measurements. The overestimated integral doses for small beam segments explain the deviations observed in the dose volume histograms. The Helax-TMS results are in better agreement with MCDE, although deviations exceeding 5% have been observed in the optical chiasm. Monte Carlo dose deviations of more than 10% as found with Peregrine are unacceptable as an influence on the clinical outcome is possible and as the purpose of Monte Carlo treatment planning is to obtain an accuracy of 2%. We would like to emphasize that only the Elekta MLC has been tested in this work, so it is certainly possible that alpha releases of Peregrine provide more accurate results for other accelerators.

Energy modulated electron beam therapy with conventional clinical accelerators has lagged behind photon IMRT despite its potential to achieve highly conformal dose distributions in superficial targets. One of the reasons for this is the absence of an automated collimating device that allows for the flexible delivery of a series of variable field openings. Electron-specific multileaf collimators attached to the bottom of the applicator require the use of a large number of motors and suffer from being relatively bulky and impractical for head and neck sites. In this work, we investigate the treatment planning aspects of a proposed 'few-leaf' electron collimator (FLEC) that consists of four motor-driven trimmer bars at the end of the applicator. The device is designed to serve as an accessory to standard equipment and allows for the shaping of any irregular field by combination of rectangular fieldlets. Using a Monte Carlo model of the FLEC, dose distributions are optimized using a simulated annealing (SA) inverse planning algorithm based on a limited number of Monte Carlo pre-generated, realistic phantom-specific dose kernels and user-specified dose–volume constraints. Using a phantom setup with an artificial target enclosed by organs at risk (OAR) as well as using a realistic patient case, we demonstrate that highly conformal distributions can be generated. Estimates of delivery times are made and show that a full treatment fraction can be kept to 15 min or less.

The aim of this work was to evaluate the accuracy of dose predicted in heterogeneous media by a pencil beam (PB), a collapsed cone (CC) and a Monte Carlo (MC) algorithm. For this purpose, a simple multi-layer phantom composed of Styrofoam and white polystyrene was irradiated with 10 × 10 cm² as well as 20 × 20 cm² open 6 MV photon fields. The beam axis was aligned parallel to the layers and various field offsets were applied. Thereby, the amount of lateral scatter was controlled. Dose measurements were performed with an ionization chamber positioned both in the central layer of white polystyrene and the adjacent layers of Styrofoam. It was found that, in white polystyrene, both MC and CC calculations agreed satisfactorily with the measurements whereas the PB algorithm calculated 12% higher doses on average. By studying off-axis dose profiles the observed differences in the calculation results increased dramatically for the three algorithms. In the regions of low density CC calculated 10% (8%) lower doses for the 10 × 10 cm² (20 × 20 cm²) fields than MC. The MC data on the other hand agreed well with the measurements, presuming that proper replacement correction for the ionization chamber embedded in Styrofoam was performed. PB results evidently did not account for the scattering geometry and were therefore not really comparable. Our investigations showed that the PB algorithm generates very large errors for the dose in the vicinity of interfaces and within low-density regions. We also found that for the used CC algorithm large deviations for the absolute dose (dose/monitor unit) occur in regions of electronic disequilibrium. The performance might be improved by better adapted parameters. Therefore, we recommend a careful investigation of the accuracy for dose calculations in heterogeneous media for each beam data set and algorithm.

The purpose of this work is to investigate the accuracy of dose calculation of a commercial treatment planning system (Corvus, Normos Corp., Sewickley, PA). In this study, 30 prostate intensity-modulated radiotherapy (IMRT) treatment plans from the commercial treatment planning system were recalculated using the Monte Carlo method. Dose–volume histograms and isodose distributions were compared. Other quantities such as minimum dose to the target (D_min), the dose received by 98% of the target volume (D₉₈), dose at the isocentre (D_iso), mean target dose (D_mean) and the maximum critical structure dose (D_max) were also evaluated based on our clinical criteria. For coplanar plans, the dose differences between Monte Carlo and the commercial treatment planning system with and without heterogeneity correction were not significant. The differences in the isocentre dose between the commercial treatment planning system and Monte Carlo simulations were less than 3% for all coplanar cases. The differences on D₉₈ were less than 2% on average. The differences in the mean dose to the target between the commercial system and Monte Carlo results were within 3%. The differences in the maximum bladder dose were within 3% for most cases. The maximum dose differences for the rectum were less than 4% for all the cases. For non-coplanar plans, the difference in the minimum target dose between the treatment planning system and Monte Carlo calculations was up to 9% if the heterogeneity correction was not applied in Corvus. This was caused by the excessive attenuation of the non-coplanar beams by the femurs. When the heterogeneity correction was applied in Corvus, the differences were reduced significantly. These results suggest that heterogeneity correction should be used in dose calculation for prostate cancer with non-coplanar beam arrangements.

The PEREGRINE Monte Carlo dose-calculation system (North American Scientific, Cranberry Township, PA) is the first commercially available Monte Carlo dose-calculation code intended specifically for intensity modulated radiotherapy (IMRT) treatment planning and quality assurance. In order to assess the impact of Monte Carlo based dose calculations for IMRT clinical cases, dose distributions for 11 head and neck patients were evaluated using both PEREGRINE and the CORVUS (North American Scientific, Cranberry Township, PA) finite size pencil beam (FSPB) algorithm with equivalent path-length (EPL) inhomogeneity correction. For the target volumes, PEREGRINE calculations predict, on average, a less than 2% difference in the calculated mean and maximum doses to the gross tumour volume (GTV) and clinical target volume (CTV). An average 16% ± 4% and 12% ± 2% reduction in the volume covered by the prescription isodose line was observed for the GTV and CTV, respectively. Overall, no significant differences were noted in the doses to the mandible and spinal cord. For the parotid glands, PEREGRINE predicted a 6% ± 1% increase in the volume of tissue receiving a dose greater than 25 Gy and an increase of 4% ± 1% in the mean dose. Similar results were noted for the brainstem where PEREGRINE predicted a 6% ± 2% increase in the mean dose. The observed differences between the PEREGRINE and CORVUS calculated dose distributions are attributed to secondary electron fluence perturbations, which are not modelled by the EPL correction, issues of organ outlining, particularly in the vicinity of air cavities, and differences in dose reporting (dose to water versus dose to tissue type).

This paper reviews the effect of statistical uncertainties on radiotherapy treatment planning using Monte Carlo simulations. We discuss issues related to the statistical analysis of Monte Carlo dose calculations for realistic clinical beams using various variance reduction or time saving techniques. We discuss the effect of statistical uncertainties on dose prescription and monitor unit calculation for conventional treatment and intensity-modulated radiotherapy (IMRT) based on Monte Carlo simulations. We show the effect of statistical uncertainties on beamlet dose calculation and plan optimization for IMRT and other advanced treatment techniques such as modulated electron radiotherapy (MERT). We provide practical guidelines for the clinical implementation of Monte Carlo treatment planning and show realistic examples of Monte Carlo based IMRT and MERT plans.

Recent studies have demonstrated that Monte Carlo (MC) denoising techniques can reduce MC radiotherapy dose computation time significantly by preferentially eliminating statistical fluctuations ('noise') through smoothing. In this study, we compare new and previously published approaches to MC denoising, including 3D wavelet threshold denoising with sub-band adaptive thresholding, content adaptive mean–median-hybrid (CAMH) filtering, locally adaptive Savitzky–Golay curve-fitting (LASG), anisotropic diffusion (AD) and an iterative reduction of noise (IRON) method formulated as an optimization problem. Several challenging phantom and computed-tomography-based MC dose distributions with varying levels of noise formed the test set. Denoising effectiveness was measured in three ways: by improvements in the mean-square-error (MSE) with respect to a reference (low noise) dose distribution; by the maximum difference from the reference distribution and by the 'Van Dyk' pass/fail criteria of either adequate agreement with the reference image in low-gradient regions (within 2% in our case) or, in high-gradient regions, a distance-to-agreement-within-2% of less than 2 mm. Results varied significantly based on the dose test case: greater reductions in MSE were observed for the relatively smoother phantom-based dose distribution (up to a factor of 16 for the LASG algorithm); smaller reductions were seen for an intensity modulated radiation therapy (IMRT) head and neck case (typically, factors of 2–4). Although several algorithms reduced statistical noise for all test geometries, the LASG method had the best MSE reduction for three of the four test geometries, and performed the best for the Van Dyk criteria. However, the wavelet thresholding method performed better for the head and neck IMRT geometry and also decreased the maximum error more effectively than LASG. In almost all cases, the evaluated methods provided acceleration of MC results towards statistically more accurate results.

Recently, the theoretical framework of the adjoint Monte Carlo (AMC) method has been developed using a simplified patient geometry. In this study, we extended our previous work by applying the AMC framework to a 3D anatomical model called VIP-Man constructed from the Visible Human images. First, the adjoint fluxes for the prostate (PTV) and rectum and bladder (organs at risk (OARs)) were calculated on a spherical surface of 1 m radius, centred at the centre of gravity of PTV. An importance ratio, defined as the PTV dose divided by the weighted OAR doses, was calculated for each of the available beamlets to select the beam angles. Finally, the detailed doses in PTV and OAR were calculated using a forward Monte Carlo simulation to include the electron transport. The dose information was then used to generate dose volume histograms (DVHs). The Pinnacle treatment planning system was also used to generate DVHs for the 3D plans with beam angles obtained from the AMC (3D-AMC) and a standard six-field conformal radiation therapy plan (3D-CRT). Results show that the DVHs for prostate from 3D-AMC and the standard 3D-CRT are very similar, showing that both methods can deliver prescribed dose to the PTV. A substantial improvement in the DVHs for bladder and rectum was found for the 3D-AMC method in comparison to those obtained from 3D-CRT. However, the 3D-AMC plan is less conformal than the 3D-CRT plan because only bladder, rectum and PTV are considered for calculating the importance ratios. Nevertheless, this study clearly demonstrated the feasibility of the AMC in selecting the beam directions as a part of a treatment planning based on the anatomical information in a 3D and realistic patient anatomy.

An important step in Monte Carlo treatment planning (MCTP), which is commonly performed uncritically, is segmentation of the patient CT data into a voxel phantom for dose calculation. In addition to assigning mass densities to voxels, as is done in conventional TP, this entails assigning media. Mis-assignment of media can potentially lead to significant dose errors in MCTP. In this work, a test phantom with exact-known composition was used to study CT segmentation errors and to quantify subsequent MCTP inaccuracies. For our test cases, we observed dose errors in some regions of up to 10% for 6 and 15 MV photons, more than 30% for an 18 MeV electron beam and more than 40% for 250 kVp photons. It is concluded that a careful CT calibration with a suitable phantom is essential. Generic calibrations and the use of commercial CT phantoms have to be critically assessed.

The aim of this project is to extend accurate and patient-specific treatment planning to new treatment modalities, such as molecular targeted radiation therapy, incorporating previously crafted and proven Monte Carlo and deterministic computation methods. A flexible software environment is being created that allows planning radiation treatment for these new modalities and combining different forms of radiation treatment with consideration of biological effects. The system uses common input interfaces, medical image sets for definition of patient geometry and dose reporting protocols. Previously, the Idaho National Engineering and Environmental Laboratory (INEEL), Montana State University (MSU) and Lawrence Livermore National Laboratory (LLNL) had accrued experience in the development and application of Monte Carlo based, three-dimensional, computational dosimetry and treatment planning tools for radiotherapy in several specialized areas. In particular, INEEL and MSU have developed computational dosimetry systems for neutron radiotherapy and neutron capture therapy, while LLNL has developed the PEREGRINE computational system for external beam photon–electron therapy. Building on that experience, the INEEL and MSU are developing the MINERVA (modality inclusive environment for radiotherapeutic variable analysis) software system as a general framework for computational dosimetry and treatment planning for a variety of emerging forms of radiotherapy. In collaboration with this development, LLNL has extended its PEREGRINE code to accommodate internal sources for molecular targeted radiotherapy (MTR), and has interfaced it with the plugin architecture of MINERVA. Results from the extended PEREGRINE code have been compared to published data from other codes, and found to be in general agreement (EGS4—2%, MCNP—10%) (Descalle et al 2003 Cancer Biother. Radiopharm.18 71–9). The code is currently being benchmarked against experimental data. The interpatient variability of the drug pharmacokinetics in MTR can only be properly accounted for by image-based, patient-specific treatment planning, as has been common in external beam radiation therapy for many years. MINERVA offers 3D Monte Carlo-based MTR treatment planning as its first integrated operational capability. The new MINERVA system will ultimately incorporate capabilities for a comprehensive list of radiation therapies. In progress are modules for external beam photon–electron therapy and boron neutron capture therapy (BNCT). Brachytherapy and proton therapy are planned. Through the open application programming interface (API), other groups can add their own modules and share them with the community.

Intensity modulated radiotherapy (IMRT) has become a treatment of choice in many oncological institutions. Small fields or beamlets with sizes of 1 to 5 cm² are now routinely used in IMRT delivery. Therefore small ionization chambers (IC) with sensitive volumes ⩽0.1 cm³are generally used for dose verification of an IMRT treatment. The measurement conditions during verification may be quite different from reference conditions normally encountered in clinical beam calibration, so dosimetry of these narrow photon beams pertains to the so-called non-reference conditions for beam calibration. This work aims at estimating the error made when measuring the organ at risk's (OAR) absolute dose by a micro ion chamber (μIC) in a typical IMRT treatment. The dose error comes from the assumption that the dosimetric parameters determining the absolute dose are the same as for the reference conditions. We have selected two clinical cases, treated by IMRT, for our dose error evaluations. Detailed geometrical simulation of the μIC and the dose verification set-up was performed. The Monte Carlo (MC) simulation allows us to calculate the dose measured by the chamber as a dose averaged over the air cavity within the ion-chamber active volume (D_air). The absorbed dose to water (D_water) is derived as the dose deposited inside the same volume, in the same geometrical position, filled and surrounded by water in the absence of the ion chamber. Therefore, the D_water/D_air dose ratio is the MC estimator of the total correction factor needed to convert the absorbed dose in air into the absorbed dose in water. The dose ratio was calculated for the μIC located at the isocentre within the OARs for both clinical cases. The clinical impact of the calculated dose error was found to be negligible for the studied IMRT treatments.

Density heterogeneities can have a profound effect on dose distributions for proton therapy. Although analytical calculations in homogeneous media are relatively straightforward, the modelling of the propagation of the beam through density heterogeneities can be more problematical. At the Paul Scherrer Institute, an in-house dedicated Monte Carlo (MC) code has been used for over a decade to assess the possible deficiencies of the analytical calculations in patient geometries. The MC code has been optimized for speed, and as such traces primary protons only through the treatment nozzle and patient's CT. Contributions from nuclear interactions are modelled analytically with no tracing of secondary particles. The MC code has been verified against measured data in water and experimental proton radiographs through a heterogeneous anthropomorphic phantom. In comparison to the analytical calculation, the MC code has been applied to both spot scanned and intensity modulated proton therapy plans, and to a number of cases containing titanium metal implants. In summary, MC-based dose calculations could provide an invaluable tool for independently verifying the calculated dose distribution within a patient geometry as part of a comprehensive quality assurance protocol for proton treatment plans.

When dosimetric effects in time-dependent geometries are studied, usually either the results of individual three-dimensional (3D) calculations are combined or probability-based approaches are applied. These methods may become cumbersome and time-consuming if high time resolution is required or if the geometry is complex. Furthermore, it is difficult to study double-dynamic systems, e.g., to investigate the influence of time-dependent beam delivery (i.e., magnetically moving beam spots in proton beam scanning) on the dose deposition in a moving target. We recently introduced the technique of 4D Monte Carlo dose calculation to model continuously changing geometries. In intensity modulated proton therapy, dose is delivered by individual pristine Bragg curves. Dose spots are positioned in the patient by varying magnetic field and beam energy. If the movement of these dose spots occurs during significant respiratory motion, interplay effects can take place. Because of the inhomogeneity of individual subfields, the consequences of motion can be more severe than in conventional proton therapy. We demonstrate how the technique of 4D Monte Carlo can be used to study interplay effects in proton beam scanning. Time-dependent beam delivery to a changing patient geometry is simulated in a single 4D dose calculation. Interplay effects between respiratory motion and beam scanning speed are demonstrated.

In high energy clinical proton beams nonelastic nuclear interactions contribute substantially to the total dose. It is therefore of importance to know these contributions quantitatively and to be able to scale them correctly as a function of Hounsfield units obtained from CT data. In this work, the second of these issues has been addressed. The importance of taking material-dependent nonelastic nuclear interactions into account has been investigated for Monte Carlo calculations. A scaling curve for nonelastic nuclear interactions as a function of Hounsfield unit has been established and compared with similar data for the stopping powers. Monte Carlo simulations using McPTRAN.MEDIA and MCNPX have been performed in homogeneous media and in inhomogeneous slab geometries. The results show that for skeletal tissues and for adipose tissue, the tissue to water nonelastic cross section ratios differ up to 10% compared to the tissue to water stopping power ratios. This results in errors of the order of 2–3% when both contributions to the total dose are scaled in the same way (with stopping power ratios). Monte Carlo simulations in slab geometries with tissue materials for 200 MeV protons show similar effects, but when both contributions are scaled correctly the errors are not larger than 0.5% in the situations investigated here.

A particle track-repeating algorithm has been developed for proton beam dose calculation for radiotherapy. Monoenergetic protons with 250 MeV kinetic energy were simulated in an infinite water phantom using the GEANT3 Monte Carlo code. The changes in location, angle and energy for every transport step and the energy deposition along the track were recorded for the primary protons and all secondary particles. When calculating dose for a patient with a realistic proton beam, the pre-generated particle tracks were repeated in the patient geometry consisting of air, soft tissue and bone. The medium and density for each dose scoring voxel in the patient geometry were derived from patient CT data. The starting point, at which a proton track was repeated, was determined according to the incident proton energy. Thus, any protons with kinetic energy less than 250 MeV can be simulated. Based on the direction of the incident proton, the tracks were first rotated and for the subsequent steps, the scattering angles were simply repeated for air and soft tissue but adjusted properly based on the scattering power for bone. The particle step lengths were adjusted based on the density for air and soft tissue and also on the stopping powers for bone while keeping the energy deposition unchanged in each step. The difference in nuclear interactions and secondary particle generation between water and these materials was ignored. The algorithm has been validated by comparing the dose distributions in uniform water and layered heterogeneous phantoms with those calculated using the GEANT3 code for 120, 150, 180 and 250 MeV proton beams. The differences between them were within 2%. The new algorithm was about 13 times faster than the GEANT3 Monte Carlo code for a uniform phantom geometry and over 700 times faster for a heterogeneous phantom geometry.

This paper describes the application of the SRNA Monte Carlo package for proton transport simulations in complex geometry and different material compositions. The SRNA package was developed for 3D dose distribution calculation in proton therapy and dosimetry and it was based on the theory of multiple scattering. The decay of proton induced compound nuclei was simulated by the Russian MSDM model and our own using ICRU 63 data. The developed package consists of two codes: the SRNA-2KG, which simulates proton transport in combinatorial geometry and the SRNA-VOX, which uses the voxelized geometry using the CT data and conversion of the Hounsfield's data to tissue elemental composition. Transition probabilities for both codes are prepared by the SRNADAT code. The simulation of the proton beam characterization by multi-layer Faraday cup, spatial distribution of positron emitters obtained by the SRNA-2KG code and intercomparison of computational codes in radiation dosimetry, indicate immediate application of the Monte Carlo techniques in clinical practice. In this paper, we briefly present the physical model implemented in the SRNA package, the ISTAR proton dose planning software, as well as the results of the numerical experiments with proton beams to obtain 3D dose distribution in the eye and breast tumour.

The main goal of external beam radiotherapy is the treatment of tumours, while sparing, as much as possible, surrounding healthy tissues. In order to master and optimize the dose distribution within the patient, dosimetric planning has to be carried out. Thus, for determining the most accurate dose distribution during treatment planning, a compromise must be found between the precision and the speed of calculation. Current techniques, using analytic methods, models and databases, are rapid but lack precision. Enhanced precision can be achieved by using calculation codes based, for example, on Monte Carlo methods. However, in spite of all efforts to optimize speed (methods and computer improvements), Monte Carlo based methods remain painfully slow. A newer way to handle all of these problems is to use a new approach in dosimetric calculation by employing neural networks. Neural networks (Wu and Zhu 2000 Phys. Med. Biol.45 913–22) provide the advantages of those various approaches while avoiding their main inconveniences, i.e., time-consumption calculations. This permits us to obtain quick and accurate results during clinical treatment planning. Currently, results obtained for a single depth–dose calculation using a Monte Carlo based code (such as BEAM (Rogers et al 2003 NRCC Report PIRS-0509(A) rev G)) require hours of computing. By contrast, the practical use of neural networks (Mathieu et al 2003 Proceedings Journées Scientifiques Francophones, SFRP) provides almost instant results and quite low errors (less than 2%) for a two-dimensional dosimetric map.

In 2002 we fully implemented clinically a commercial Monte Carlo based treatment planning system for electron beams. The software, developed by MDS Nordion (presently Nucletron), is based on Kawrakow's VMC++ algorithm. The Monte Carlo module is integrated with our Theraplan Plus™ treatment planning system. An extensive commissioning process preceded clinical implementation of this software. Using a single virtual 'machine' for each electron beam energy, we can now calculate very accurately the dose distributions and the number of MU for any arbitrary field shape and SSD. This new treatment planning capability has significantly impacted our clinical practice. Since we are more confident of the actual dose delivered to a patient, we now calculate accurate three-dimensional (3D) dose distributions for a greater variety of techniques and anatomical sites than we have in the past. We use the Monte Carlo module to calculate dose for head and neck, breast, chest wall and abdominal treatments with electron beams applied either solo or in conjunction with photons. In some cases patient treatment decisions have been changed, as compared to how such patients would have been treated in the past. In this paper, we present the planning procedure and some clinical examples.