Experimental test of Monte Carlo proton transport at grazing incidence in GEANT4, FLUKA and MCNPX

Comparing 2 Monte Carlo Systems in Use for Proton Therapy Research

Purpose

Several Monte Carlo transport codes are available for medical physics users. To ensure confidence in the accuracy of the codes, they must be continually cross-validated. This study provides comparisons between MC² and Tool for Particle Simulation (TOPAS) simulations, that is, between medical physics applications for Monte Carlo N-Particle Transport Code (MCNPX) and Geant4.

Materials and Methods

Monte Carlo simulations were repeated with 2 wrapper codes: TOPAS (based on Geant4) and MC² (based on MCNPX). Simulations increased in geometrical complexity from a monoenergetic beam incident on a water phantom, to a monoenergetic beam incident on a water phantom with a bone or tissue slab at various depths, to a spread-out Bragg peak incident on a voxelized computed tomography (CT) geometry. The CT geometry cases consisted of head and neck tissue and lung tissue. The results of the simulations were compared with one another through dose or energy deposition profiles, r ₉₀ calculations, and γ-analyses.

Results

Both codes gave very similar results with monoenergetic beams incident on a water phantom. Systematic differences were observed between MC² and TOPAS simulations when using a lung or bone slab in a water phantom, particularly in the r ₉₀ values, where TOPAS consistently calculated r ₉₀ to be deeper by about 0.4%. When comparing the performance of the 2 codes in a CT geometry, the results were still very similar, exemplified by a 3-dimensional γ-analysis pass rate > 95% at the 2%-2-mm criterion for tissues from both head and neck and lung.

Conclusion

Differences between TOPAS and MC² were minor and were not considered clinically relevant.

The physics of proton therapy

The physics of proton therapy has advanced considerably since it was proposed in 1946. Today analytical equations and numerical simulation methods are available to predict and characterize many aspects of proton therapy. This article reviews the basic aspects of the physics of proton therapy, including proton interaction mechanisms, proton transport calculations, the determination of dose from therapeutic and stray radiations, and shielding design. The article discusses underlying processes as well as selected practical experimental and theoretical methods. We conclude by briefly speculating on possible future areas of research of relevance to the physics of proton therapy.

A fast Monte Carlo code for proton transport in radiation therapy based on MCNPX

An important requirement for proton therapy is a software for dose calculation. Monte Carlo is the most accurate method for dose calculation, but it is very slow. In this work, a method is developed to improve the speed of dose calculation. The method is based on pre-generated tracks for particle transport. The MCNPX code has been used for generation of tracks. A set of data including the track of the particle was produced in each particular material (water, air, lung tissue, bone, and soft tissue). This code can transport protons in wide range of energies (up to 200 MeV for proton). The validity of the fast Monte Carlo (MC) code is evaluated with data MCNPX as a reference code. While analytical pencil beam algorithm transport shows great errors (up to 10%) near small high density heterogeneities, there was less than 2% deviation of MCNPX results in our dose calculation and isodose distribution. In terms of speed, the code runs 200 times faster than MCNPX. In the Fast MC code which is developed in this work, it takes the system less than 2 minutes to calculate dose for 10(6) particles in an Intel Core 2 Duo 2.66 GHZ desktop computer.

The energy margin strategy for reducing dose variation due to setup uncertainty in intensity modulated proton therapy (IMPT) delivered with distal edge tracking (DET)

Intensity-modulated proton therapy (IMPT) can produce plans with similar target dose conformity but lower normal tissue dose than intensity-modulated X-ray therapy (IMXT). However, due to the finite range of proton beams in tissue, proton therapy treatment plans are usually more sensitive to setup uncertainties than X-ray therapy plans. In this work, the energy margin (EM) concept, which was initially developed for passive scattering proton therapy, was generalized to apply to IMPT treatment planning. The effectiveness of the EM method was evaluated on five head-and-neck cancer patients with distal edge tracking (DET) treatment plans by comparing the original plans (ORG) without an EM to those with an EM. Three beam arrangements were considered: 24 beams delivered over a 360° arc, 12 beams delivered over a 180° arc, and 12 beams delivered over two 90° fan angles. Setup uncertainty was modeled by sampling rigid translational shifts from a Gaussian distribution with a mean of 0 mm and standard deviation of 2 mm in all directions. Delivered dose distributions for all 30 fractions were recalculated using the Geant4 Monte Carlo code. Normalized total dose (NTD) for both the CTV and a ring structure surrounding the PTV were recorded. The plan quality comparison revealed that EM plans had the same CTV coverage but higher dose to the normal tissue than ORG plans. After the simulated delivery, ORG plans resulted in more than 3% underdosage to 5% of the CTV volume in all three beam arrangements, whereas the EM plans did not. Both ORG and EM plans did not produce more than 5% overdose to D2% of the ring structure. The use of an EM for IMPT treatment planning can substantially reduce sensitivity of the resulting dose distributions to setup uncertainty.

Range uncertainties in proton therapy and the role of Monte Carlo simulations

The main advantages of proton therapy are the reduced total energy deposited in the patient as compared to photon techniques and the finite range of the proton beam. The latter adds an additional degree of freedom to treatment planning. The range in tissue is associated with considerable uncertainties caused by imaging, patient setup, beam delivery and dose calculation. Reducing the uncertainties would allow a reduction of the treatment volume and thus allow a better utilization of the advantages of protons. This paper summarizes the role of Monte Carlo simulations when aiming at a reduction of range uncertainties in proton therapy. Differences in dose calculation when comparing Monte Carlo with analytical algorithms are analyzed as well as range uncertainties due to material constants and CT conversion. Range uncertainties due to biological effects and the role of Monte Carlo for in vivo range verification are discussed. Furthermore, the current range uncertainty recipes used at several proton therapy facilities are revisited. We conclude that a significant impact of Monte Carlo dose calculation can be expected in complex geometries where local range uncertainties due to multiple Coulomb scattering will reduce the accuracy of analytical algorithms. In these cases Monte Carlo techniques might reduce the range uncertainty by several mm.

Range uncertainties in proton therapy and the role of Monte Carlo simulations

The main advantages of proton therapy are the reduced total energy deposited in the patient as compared to photon techniques and the finite range of the proton beam. The latter adds an additional degree of freedom to treatment planning. The range in tissue is associated with considerable uncertainties caused by imaging, patient setup, beam delivery and dose calculation. Reducing the uncertainties would allow a reduction of the treatment volume and thus allow a better utilization of the advantages of protons. This paper summarizes the role of Monte Carlo simulations when aiming at a reduction of range uncertainties in proton therapy. Differences in dose calculation when comparing Monte Carlo with analytical algorithms are analyzed as well as range uncertainties due to material constants and CT conversion. Range uncertainties due to biological effects and the role of Monte Carlo for in vivo range verification are discussed. Furthermore, the current range uncertainty recipes used at several proton therapy facilities are revisited. We conclude that a significant impact of Monte Carlo dose calculation can be expected in complex geometries where local range uncertainties due to multiple Coulomb scattering will reduce the accuracy of analytical algorithms. In these cases Monte Carlo techniques might reduce the range uncertainty by several mm.

Parametrization and application of scatter kernels for modelling scanned proton beam collimator scatter dose

Collimators are routinely used in proton radiotherapy to laterally confine the field and improve the penumbra. Collimator scatter contributes up to 15% of the local dose and is therefore important to include in treatment planning dose calculation. We present a method for reconstruction of the collimator scatter phase space based on the parametrization of pre-calculated scatter kernels. Collimator scatter distributions, generated by the Monte Carlo (MC) package GEANT4.8.2, were scored differential in direction and energy. The distributions were then parametrized so as to enable a fast reconstruction by sampling. MC calculated dose distributions in water based on the parametrized phase space were compared to full MC simulations that included the collimator in the simulation geometry, as well as to experimental data. The experiments were performed at the scanned proton beam line at the The Svedberg Laboratory (TSL) in Uppsala, Sweden. Dose calculations using the parametrization of this work and the full MC for isolated typical cases of collimator scatter were compared by means of the gamma index. The result showed that in total 96.7% (99.3%) of the voxels fulfilled the gamma 2.0%/2.0 mm (3.0%/3.0 mm) criterion. The dose distribution for a collimated field was calculated based on the phase space created by the collimator scatter model incorporated into the generation of the phase space of a scanned proton beam. Comparing these dose distributions to full MC simulations, including particle transport in the MLC, yielded that in total for 18 different collimated fields, 99.1% of the voxels satisfied the gamma 1.0%/1.0 mm criterion and no voxel exceeded the gamma 2.6%/2.6 mm criterion. The dose contribution of collimator scatter along the central axis as predicted by the model showed good agreement with experimental data.